Causal Layer Reframing

batch: First Iteration/Batch 34

Causal Layered Analysis

notes: Recognized method name; captured as a variant and near name, not drafted separately.

CLA

slug: cla

notes: Abbreviation for causal layered analysis.

Layered Reframing

notes: Plain-language near name when the four-layer logic remains explicit.

Litany-System-Worldview-Myth Analysis

notes: Descriptive near name for the classic layer structure.

slug: sensemaking

slug: framing

slug: metaphor

slug: causality

slug: holism

slug: representation

slug: uncertainty

components:
Focal Issue Boundary
Surface Litany
Systemic Cause
Discourse / Worldview Frame
Myth / Metaphor Layer
Layer-Specific Intervention
Cross-Layer Translation
Reframe Choice and Action Link
Interpretive Voice Check
Layer Revision Review
Historical Origin Trace
Counter-Frame Inventory
Implementation Owner Map

mechanisms:
Causal Layered Analysis Template
Layered Reframing Workshop
Litany-to-System Mapping Session
Discourse / Worldview Analysis Memo
Myth / Metaphor Mapping Canvas
Layer-Specific Intervention Matrix
Narrative Reframing Brief
Cross-Layer Scenario Comparison
Cultural Diagnosis Interview Protocol

essence_one_line: Reframe an issue across surface events, systemic causes, worldviews, and deep narratives to unlock different interventions.

compression_statement: When a problem is stuck at the surface-event level, analyze deeper causal and interpretive layers to reveal structural, worldview, and narrative shifts that could change the future.

core_intuition: People often treat the most visible layer of a problem as the whole problem. Layered reframing slows the rush to action long enough to ask what system keeps producing the symptom, what worldview makes that system seem normal, and what deep story makes some futures imaginable while hiding others.

canonical_formula: focal issue + surface litany + systemic causes + discourse/worldview + myth/metaphor + layer-specific interventions + cross-layer translation -> reframed action logic

structural_problem: Actors respond to visible symptoms while deeper systems, assumptions, discourses, or myths keep reproducing the issue.

condition: The same visible problem keeps recurring after surface fixes.

diagnostic_question: Do interventions reduce symptoms temporarily while the underlying pattern returns?

condition: The dominant frame makes only a narrow set of actions seem reasonable.

diagnostic_question: Are people treating one worldview, category system, or metaphor as common sense rather than as a choice?

condition: Stakeholders disagree about what kind of problem this is.

diagnostic_question: Do different groups locate the problem at different levels, such as individual behavior, institutions, culture, or identity?

condition: A futures or strategy process is stuck in trend description.

diagnostic_question: Are participants naming events and trends without changing their assumptions about possibility or action?

condition: Structural analysis alone is not changing behavior or legitimacy.

diagnostic_question: Have systems maps identified causes while people still resist the implied intervention because the deeper story is unchanged?

condition: A decision requires both diagnosis and reframing.

diagnostic_question: Will a better intervention require changing how the issue is interpreted, not only changing resources or processes?

symptom: Symptom chasing

description: Teams repeatedly respond to headlines, incidents, metrics, complaints, or crises without altering the structures that regenerate them.

symptom: Single-frame debate

description: Argument continues within one frame, so opposing sides fight over conclusions while leaving deeper assumptions untouched.

symptom: Systems map without imagination

description: The structural causes are known, but the available future still feels fixed, fatalistic, or politically impossible.

symptom: Narrative mismatch

description: The proposed intervention is technically sound but clashes with the identities, metaphors, or legitimacy stories through which people understand the issue.

symptom: Deep story invisibility

description: Language such as growth, risk, care, progress, security, scarcity, or efficiency quietly determines what options seem natural.

symptom: Workshop epiphany decay

description: Participants have a strong reframing moment, but it does not translate into decisions, policy, design, or practice.

root_tension: The deeper the frame, the more it shapes what actors notice and imagine; but the deeper the frame, the harder it is to make evidence-based, action-linked, and ethically legitimate. The archetype must reveal deeper layers without turning interpretation into unfalsifiable storytelling or manipulation.

condition: The issue is already well understood and only needs execution capacity.

why_not: Implementation discipline, resource allocation, or accountability may be primary; layered reframing can become delay.

condition: The needed change is a narrow causal correction within a known mechanism.

why_not: Root-cause analysis, process correction, or reliability engineering may be sufficient.

condition: The exercise only produces a slogan, vision phrase, or brand narrative.

why_not: A narrative artifact without layer diagnosis and action translation is not the archetype.

condition: Participants are unwilling to examine assumptions or power.

why_not: The method may produce safe surface language while leaving worldview and myth layers untouched.

condition: The problem requires rapid emergency response before reflection.

why_not: Immediate stabilization may come first; layered reframing can follow once urgency allows.

condition: The facilitator already knows the desired reframe and is using the process to persuade.

why_not: That is manipulation or communication strategy, not genuine layered reframing.

structural_intervention: Map the surface litany, systemic causes, discourse/worldview, and myth/metaphor layers; then generate and connect interventions at each layer.

Set the focal issue boundary

logic: Name the issue, decision, or future question precisely enough that every layer remains about the same pattern.

Record the surface litany

logic: Capture the visible symptoms, events, data, complaints, headlines, or immediate narratives without dismissing them.

Identify systemic causes

logic: Ask what structures, incentives, infrastructures, policies, technologies, institutions, or recurring processes produce the surface pattern.

Surface discourse and worldview

logic: Name the assumptions, categories, values, identities, and legitimacy claims that make the system appear natural, inevitable, or contested.

Elicit myth and metaphor

logic: Identify deep stories, metaphors, and images that organize emotional orientation and possibility space.

Generate layer-specific interventions

logic: Design actions that fit each layer rather than forcing all action into surface fixes or structural reform.

Translate across layers

logic: Check how interventions at one layer require support or communication at other layers.

Choose a reframe linked to action

logic: Select the reframing that changes decision criteria, strategy, design, governance, communication, or learning practice.

Review fit and consequences

logic: Revisit the layer map as action unfolds, paying attention to excluded voices, unintended consequences, and whether symptoms actually change.

rule: Do not skip the surface layer.

reason: Visible evidence anchors the exercise and prevents abstract reframing from drifting away from lived or measured reality.

rule: Do not stop at the systemic layer when legitimacy or imagination is the constraint.

reason: Structural causes may be known but politically, culturally, or emotionally blocked by deeper frames.

rule: Treat deeper layers as interpretive, not automatically truer.

reason: Myths and metaphors can be illuminating, but they require humility, contestability, and action-testing.

rule: Generate interventions at multiple layers before choosing.

reason: The point of reframing is to enlarge the action repertoire, not to crown one preferred explanation.

rule: Preserve affected voices in worldview and narrative work.

reason: Deep reframing can erase people when it is performed only by sponsors, experts, or outside analysts.

rule: Tie the selected reframe to concrete decisions.

reason: A reframe that does not change priorities, commitments, design, policy, or behavior remains descriptive.

invariant: Layer distinction

why_it_matters: Each layer reveals different causes and actions; flattening them collapses the archetype into ordinary analysis.

invariant: Surface accountability

why_it_matters: Deeper interpretation must remain accountable to observed symptoms, affected experience, and practical outcomes.

invariant: Action linkage

why_it_matters: Layered reframing is a solution archetype only when it changes intervention choice.

invariant: Interpretive plurality

why_it_matters: Multiple worldviews and stories should be made visible, especially where power determines whose interpretation counts.

invariant: Non-manipulation

why_it_matters: Changing narratives can shape behavior; ethical use requires transparency and respect for agency.

invariant: Cross-layer coherence

why_it_matters: Surface actions, system reforms, worldview shifts, and narrative changes should support rather than undermine one another.

outcome: Expanded intervention repertoire

description: Teams see stabilizing, structural, interpretive, and narrative options rather than one class of fix.

outcome: Reduced symptom-only action

description: The intervention exposes why surface fixes keep failing or require deeper supports.

outcome: Clearer strategic imagination

description: Alternative futures become more imaginable because hidden assumptions and metaphors are named.

outcome: Better fit between strategy and culture

description: Actions are more likely to be understood, accepted, or productively contested because worldview and narrative layers are considered.

outcome: More robust sensemaking

description: Participants can distinguish events, structures, frames, and deep stories instead of mixing them in one argument.

outcome: More ethical reframing

description: Voice checks and action review reduce the risk that deep narratives are imposed by powerful actors.

prose_essence:
Causal Layer Reframing is a way to stop treating the visible form of a problem as the whole problem. It asks: what is happening on the surface, what system keeps producing it, what worldview makes that system seem normal, and what deep metaphor or story shapes what people believe is possible?

The archetype is especially useful in foresight and future strategy because many future possibilities are blocked less by lack of imagination than by unexamined assumptions. When the frame changes, the available future changes: not magically, but because different interventions, responsibilities, and design choices become thinkable.

prose_when_to_use:
Use this archetype when a problem keeps returning after surface-level fixes, when stakeholders disagree because they are operating from different assumptions, or when a futures process has become a list of trends rather than a challenge to the way the issue is understood.

It is also useful when a technically sound strategy is culturally stuck. In those cases, the missing piece may not be another plan; it may be a shift in legitimacy, worldview, identity, or deep narrative that lets the plan make sense.

prose_structural_problem:
The structural problem is layer collapse. Events, systems, assumptions, and stories are all mixed together, so people argue within a single frame and keep choosing familiar interventions. Visible symptoms receive attention because they are measurable, urgent, or politically salient, while deeper structures and narratives continue to regenerate the same pattern.

This produces a characteristic failure: better evidence does not change action because the frame that interprets the evidence remains unchanged.

prose_intervention_logic:
The intervention moves deliberately through layers. First, it records the surface litany: the visible events, metrics, complaints, and symptoms. Second, it asks what systems produce those symptoms. Third, it identifies the discourse or worldview that makes those systems appear normal, legitimate, inevitable, or contested. Fourth, it elicits the deep metaphors and myths that organize emotional orientation and future possibility.

The process then comes back upward. It generates layer-specific interventions and checks how surface actions, system reforms, worldview shifts, and narrative changes can support one another. The goal is not to admire depth; the goal is to change action.

prose_key_components:
### Focal Issue Boundary

Defines the issue, decision, or future question that will be reframed across layers. The boundary keeps the process from becoming a free-floating cultural discussion. It should be narrow enough for action but broad enough to include systemic and narrative causes.

### Surface Litany

Captures the visible events, statistics, headlines, complaints, symptoms, or immediate facts that make the issue salient. This layer is deliberately not dismissed; it anchors deeper reframing in observable experience while preventing visible symptoms from being mistaken for the whole problem.

### Systemic Cause

Identifies the institutional, economic, technical, ecological, procedural, or structural arrangements that reproduce the surface pattern. Systemic causes explain recurrence. They are different from single causes, blame targets, or one-time triggers because they describe how the pattern is continually regenerated.

### Discourse / Worldview Frame

Surfaces the assumptions, categories, identities, legitimating stories, and ways of knowing that make the system appear natural or necessary. This component shows why some options seem obvious while others seem unthinkable. It should include contested and minority frames, not only the dominant worldview.

### Myth / Metaphor Layer

Names the deep images, metaphors, cultural stories, or archetypal narratives that silently guide interpretation and aspiration. The point is not to invent slogans. It is to expose deep narrative commitments that shape what futures feel plausible, desirable, threatening, or impossible.

### Layer-Specific Intervention

Defines different intervention options for the surface, systemic, worldview, and myth/metaphor layers. The archetype becomes practical only when each layer can suggest action. Surface actions may stabilize, systemic actions restructure, worldview actions shift legitimacy, and myth/metaphor actions reshape possibility.

### Cross-Layer Translation

Connects insights and actions across layers so they reinforce rather than contradict one another. A narrative shift without structural change can become rhetoric; structural reform without worldview work can be rejected or misunderstood. Translation preserves coherence.

### Reframe Choice and Action Link

Selects the reframing that will guide present action, decision criteria, design priorities, or future strategy. Layered analysis should culminate in a chosen action logic. Without an explicit action link, the intervention becomes interpretive commentary rather than a solution archetype.

### Interpretive Voice Check

Checks whose experiences, worldviews, metaphors, and stakes are represented or excluded in the reframing process. Deep frames can reproduce power. This component guards against treating one group’s worldview as universal or using reframing to erase affected voices.

### Layer Revision Review

Revisits the layers after action or new evidence to see whether the reframing still explains the issue and improves intervention choice. Reframing is not one-and-done. If surface symptoms change, systemic conditions shift, or narratives lose fit, the layer map should be updated.

prose_common_mechanisms:
Mechanisms are implementation machinery. They can help teams perform layered reframing, but they are not the archetype unless they preserve the layered movement and change intervention choice.

### Causal Layered Analysis Template

This is a template that implements part of Causal Layer Reframing. Provides a structured worksheet for recording surface litany, systemic causes, worldview/discourse, myth/metaphor, and possible interventions at each layer. It should not be confused with the archetype itself: The template is a concrete artifact. The archetype is the transferable logic of moving among layers to change intervention possibilities.

### Layered Reframing Workshop

This is a ritual that implements part of Causal Layer Reframing. Brings participants through the layers collaboratively so different surface observations, structural explanations, and narratives become visible. It should not be confused with the archetype itself: A workshop is a delivery format. It only implements the archetype when it preserves layered diagnosis and action translation.

### Litany-to-System Mapping Session

This is a method that implements part of Causal Layer Reframing. Moves from visible symptoms toward the recurring systems that generate them, usually before worldview or myth work begins. It should not be confused with the archetype itself: This mechanism implements part of the archetype but does not address all layers or select cross-layer interventions by itself.

### Discourse / Worldview Analysis Memo

This is a document that implements part of Causal Layer Reframing. Documents the assumptions, categories, legitimating arguments, and identity frames shaping the issue. It should not be confused with the archetype itself: The memo records an interpretive layer. The archetype requires connecting that layer to systemic, surface, and narrative interventions.

### Myth / Metaphor Mapping Canvas

This is a template that implements part of Causal Layer Reframing. Elicits the deep metaphors, stories, images, and archetypal roles through which people imagine the issue and possible futures. It should not be confused with the archetype itself: The canvas is a tool for one layer. It becomes useful only when tied back to action and other layers.

### Layer-Specific Intervention Matrix

This is a artifact that implements part of Causal Layer Reframing. Compares possible actions at each layer and shows which combinations may reinforce one another. It should not be confused with the archetype itself: The matrix is a decision artifact, not the full intervention pattern.

### Narrative Reframing Brief

This is a document that implements part of Causal Layer Reframing. Translates the selected deeper frame into communication, strategy, policy, design, or facilitation guidance. It should not be confused with the archetype itself: A brief can carry the output of reframing, but the archetype is the disciplined layered process that justifies the reframe.

### Cross-Layer Scenario Comparison

This is a method that implements part of Causal Layer Reframing. Compares how alternative futures look when different systemic, worldview, or myth/metaphor layers are emphasized. It should not be confused with the archetype itself: This mechanism can support futures work, but causal layer reframing remains distinct from scenario planning because its decisive move is layered interpretation.

### Cultural Diagnosis Interview Protocol

This is a protocol that implements part of Causal Layer Reframing. Collects narratives, metaphors, assumptions, lived experience, and contested meanings from people affected by the issue. It should not be confused with the archetype itself: Interviewing supplies input for deeper layers; it does not itself define the layered reframing intervention.

prose_failure_modes:
A common failure mode is the layer-label ritual: people fill out a four-box template but keep the same strategy. Another is deepest-layer absolutism, where myth and metaphor are treated as more real than evidence or systems. The mitigation is to keep every layer accountable to action and to surface outcomes.

Other failures include narrative manipulation, over-interpretation by analysts, endless diagnosis, surface neglect, expert capture of the frame, and cross-layer incoherence. The strongest safeguards are voice checks, evidence grounding, layer-specific action requirements, and scheduled review after implementation.

prose_neighbor_distinctions:
Causal Layer Reframing is close to Frame Shift Intervention, but it is not any frame shift. Its distinctive structure is the movement among surface, systemic, worldview/discourse, and myth/metaphor layers.

It is close to Structured Sensemaking, but sensemaking can organize ambiguity in many ways; this archetype uses a specific depth-layer model and requires layer-specific interventions. It is close to root-cause analysis, but root-cause analysis often stops at systems and mechanisms, while this archetype also examines worldviews and deep narratives.

It is also distinct from Scenario Portfolio Planning and Consequence Cascade Mapping. Scenario planning compares plausible futures; consequence cascade mapping follows effects outward. Causal Layer Reframing changes the interpretive depth at which the issue is understood.

prose_cross_domain_examples:
In climate adaptation, flood or heat emergencies can be reframed from isolated events to infrastructure systems, growth assumptions, and stewardship narratives. In organizational transformation, low adoption can be reframed from resistance to workflow misfit, identity threat, and a machine-versus-craft story.

In public health, missed appointments can be reframed from noncompliance to access systems, trust frames, and care-as-partnership narratives. In education, performance gaps can be reframed from student deficits to assessment design, beliefs about intelligence, and learner identity. In technology governance, data extraction can be reframed as an entrusted relationship, changing consent, retention, and accountability.

prose_non_examples:
A root-cause diagram that identifies one failed procedure is not Causal Layer Reframing. A new slogan is not Causal Layer Reframing. A scenario matrix is not Causal Layer Reframing. A values workshop is not Causal Layer Reframing unless it diagnoses the layers and changes action.

The decisive test is whether the intervention changes what actions are possible by moving across surface, system, worldview, and myth/metaphor layers.

prose_tradeoffs:
The main tradeoff is depth versus speed. Deep reframing can produce better strategy, but it can also delay necessary action if used indiscriminately. Another tradeoff is interpretive richness versus decision clarity: multiple frames are useful, but the process eventually needs a chosen action logic.

There is also a serious ethical tradeoff. Narrative reframing is powerful because it changes meaning, legitimacy, and motivation. That same power can become manipulation if sponsors use it to make people accept harm, obscure material failures, or overwrite affected voices.

prose_invariants_to_preserve:
Preserve layer distinction: the surface, system, worldview, and myth/metaphor layers reveal different kinds of constraints. Preserve surface accountability: deeper interpretations should not dismiss lived or measured reality. Preserve action linkage: a reframe must change decisions, commitments, design, or practice.

Also preserve interpretive plurality. Deep frames are not neutral; they often reflect power, identity, and history. The archetype should expose and test frames, not impose one authoritative story under the cover of analysis.

prose_target_outcomes:
A successful use of the archetype expands the intervention repertoire. Teams see more than crisis response or process improvement; they see structural reform, legitimacy work, narrative change, and coordinated cross-layer action.

It should also reduce symptom-only action, clarify why some strategies feel impossible, make hidden assumptions explicit, and improve fit between strategy and culture. In foresight contexts, it helps people imagine futures that were previously blocked by the existing frame.

prose_parameter_dimensions:
The first tuning dimension is **layer depth**. Some situations need a quick movement from symptom to system; others need serious worldview and myth/metaphor work. Going deeper can reveal hidden constraints, but it also increases ambiguity and ethical responsibility.

The second dimension is **participation breadth**. A small expert group can move quickly, but deep frames are often distributed across affected communities. Broader participation improves legitimacy and insight, while requiring more facilitation and conflict handling.

The third dimension is **evidence standard by layer**. Surface layers may rely on metrics and incidents; system layers may use process maps and institutional analysis; worldview layers may use discourse, history, and interview evidence; myth/metaphor layers require careful interpretation rather than overconfident claims.

Other tuning dimensions include time horizon, facilitation intensity, narrative explicitness, action authority, power sensitivity, revision cadence, and how directly the selected reframe will be translated into policy, design, communication, or governance.

prose_variants_and_near_names:
### Classic Causal Layered Analysis

Uses the conventional litany, system, worldview/discourse, and myth/metaphor layers to reframe a futures issue. It remains under the parent because It uses the same core intervention: move across layers to create new action possibilities.

### Myth / Metaphor Reframing

Focuses on replacing or revising the deep metaphor or narrative that constrains possible futures. It remains under the parent because It must remain accountable to the other layers and to action, otherwise it becomes branding or rhetoric.

### Discourse / Worldview Reframing

Changes the interpretive categories, legitimacy assumptions, or worldview through which the issue is understood. It remains under the parent because The worldview shift still needs surface grounding, systemic implications, and action linkage.

### Participatory Layered Reframing

Uses multiple affected voices to build and challenge the layer map so reframing is not monopolized by analysts or sponsors. It remains under the parent because It still uses the same layered movement from surface to system to worldview to myth/metaphor and back to action.

Near names include Causal Layered Analysis, CLA, Layered Reframing, and Litany-System-Worldview-Myth Analysis. These should point back to this archetype or its recognized variants. Templates, workshops, memos, and canvases should remain mechanisms unless the transferable intervention logic is present.

Classic Causal Layered Analysis

distinctive_feature: Keeps the named four-layer structure visible as the organizing scaffold.

distinct_from_parent: The parent archetype is the transferable layered reframing logic; this variant is the classic named CLA method family.

still_same_parent_because: It uses the same core intervention: move across layers to create new action possibilities.

use_when: A foresight, policy, strategy, or learning process needs the full four-layer structure.

use_when: Participants are stuck at the visible-event or trend-description level.

use_when: The goal is to generate different intervention logics, not simply to describe the problem.

Surface Litany

role: visible symptom layer

Systemic Cause

role: structural layer

Discourse / Worldview Frame

role: interpretive layer

Myth / Metaphor Layer

role: deep narrative layer

Causal Layered Analysis Template

role: four-layer recording scaffold

Layered Reframing Workshop

role: facilitated elicitation and synthesis

example_domains:
strategic foresight
policy design
organizational transformation
sustainability transitions

domain: climate adaptation

example: A team reframes flood risk from isolated disasters to infrastructure systems, land-use worldview, and a stewardship metaphor.

domain: organizational change

example: A company reframes low adoption from user resistance to workflow incentives, identity threats, and a machine-versus-craft narrative.

failure_modes: Using the four boxes as a worksheet ritual without changing intervention choice.

failure_modes: Treating the deepest layer as automatically more true than the surface layer.

Myth / Metaphor Reframing

short_definition: Focuses on replacing or revising the deep metaphor or narrative that constrains possible futures.

distinctive_feature: The key lever is deep narrative imagination rather than structural mapping alone.

distinct_from_parent: The parent moves across all layers; this variant emphasizes the deepest narrative layer while still checking surface and system fit.

still_same_parent_because: It must remain accountable to the other layers and to action, otherwise it becomes branding or rhetoric.

source_primes: metaphor grand_narrative framing

use_when: People can describe the surface and system but remain trapped by a fatalistic, adversarial, extractive, or deficit-based story.

use_when: Available actions change when the deep metaphor changes.

use_when: The intervention must reshape what feels possible, legitimate, or desirable.

Myth / Metaphor Layer

role: deep narrative lever

Interpretive Voice Check

role: guards against imposed narratives

Reframe Choice and Action Link

role: connects story to choices

Myth / Metaphor Mapping Canvas

role: elicits and compares deep images

Narrative Reframing Brief

role: translates selected narrative into action guidance

example_domains:
community planning
education
health communication
organizational identity

domain: education

example: Reframing students from empty vessels to co-investigators changes pedagogy, assessment, and classroom norms.

domain: public health

example: Reframing prevention from compliance to mutual care changes outreach and service design.

failure_modes: Beautiful metaphors that do not change systems or decisions.

Discourse / Worldview Reframing

variant_type: other

distinctive_feature: The key lever is the worldview or discourse layer that organizes what counts as common sense.

distinct_from_parent: The parent includes this layer as part of a broader four-layer movement; this variant foregrounds the discourse/worldview shift.

still_same_parent_because: The worldview shift still needs surface grounding, systemic implications, and action linkage.

use_when: Actors disagree because they inhabit different assumptions about what counts as evidence, responsibility, success, or normality.

use_when: Dominant categories exclude affected experiences or alternative futures.

use_when: The practical intervention depends on changing legitimacy or interpretation, not only resources.

Discourse / Worldview Frame

role: identifies categories and assumptions

Interpretive Voice Check

role: includes contested perspectives

Discourse / Worldview Analysis Memo

role: records assumptions and legitimacy claims

Cultural Diagnosis Interview Protocol

role: collects multiple meaning systems

example_domains:
policy design
community governance
organizational culture
technology ethics

domain: technology ethics

example: A product team reframes user data from extractable resource to entrusted relationship, changing consent and design rules.

failure_modes: Endless discourse analysis without design or governance consequences.

Participatory Layered Reframing

distinctive_feature: The process design emphasizes voice, inclusion, and contestation as part of the reframing logic.

distinct_from_parent: The parent does not require participatory governance; this variant makes participation central.

use_when: The issue affects groups whose experiences are often interpreted by others.

use_when: Deep narratives are contested or politically sensitive.

use_when: Legitimacy, trust, or justice requires participatory interpretation.

Interpretive Voice Check

role: governs inclusion and contestation

Cross-Layer Translation

role: connects interpretations across groups

Layered Reframing Workshop

role: facilitated collective analysis

Cultural Diagnosis Interview Protocol

role: collects affected narratives

example_domains:
community planning
public services
education equity
workplace culture

domain: urban planning

example: Residents, planners, and service providers build separate layer maps before negotiating a shared reframe.

failure_modes: Facilitation that suppresses conflict to create neat synthesis.

near_names:
Causal Layered Analysis
CLA
Layered Reframing
Litany-System-Worldview-Myth Analysis
Strategic Reframing Workshop
Cultural Diagnosis


Causal Mechanism Mapping

type: archetype

source: generated

batch: First Iteration/Batch 42

source_primes: causality confounding

slug: confounding

slug: counterfactuals

components:
Candidate Cause
Target Effect
Mechanism Chain
Causal Evidence Record
Confounder Check
Rival Explanation Set
Causal Claim Scope
Boundary Condition
Intervention Point
Causal Test
Counterfactual Anchor
Feedback Monitor
Mediator Map
Moderator Condition
Dose-Response Trace
Responsibility Boundary

mechanisms:
Causal Diagram
Mechanism Map
Root-Cause Analysis
Causal-Loop Map
Process Tracing
Intervention Test
Causal Inference Review
Theory of Change Model
Contribution Analysis
Failure Tree Analysis

essence_one_line: Map the mechanism connecting a proposed cause to an effect before intervening.

compression_statement: When action depends on a causal claim, Causal Mechanism Mapping identifies the candidate cause, target effect, intermediate pathway, evidence for each link, confounders and rival explanations, boundary conditions, intervention point, and test plan so the resulting action targets a real causal pathway rather than a correlation, timeline, or convenient story.

core_intuition: A causal claim is actionable only when people can say how the cause would produce the effect and what would change if they intervened on the pathway. Mapping the mechanism does not require perfect certainty, but it forces the team to replace vague attribution with a chain that can be challenged, improved, and tested.

canonical_formula: candidate_cause + target_effect -> mechanism_chain + evidence_by_link + confounder_check + boundary_conditions + intervention_point + causal_test

structural_problem: Actors treat a relationship as causal because it is correlated, sequential, plausible, emotionally salient, or politically useful, but they have not specified the pathway through which the proposed cause would produce the target effect. As a result, interventions target visible symptoms, convenient levers, or blamed actors instead of the mechanism that actually drives the outcome.

condition: A proposed cause is being used to justify action.

example: A city plans to expand a youth program because participating students have lower dropout rates, but it has not mapped whether mentoring, attendance, peer effects, selection, or family support explains the difference.

condition: A metric changes after a decision and the team assumes the decision caused it.

example: A product team attributes churn reduction to a new feature without checking whether seasonality, user mix, pricing changes, or support improvements also changed.

condition: An incident review names a root cause but cannot explain the path from condition to failure.

example: An outage report says “human error” caused the failure but does not map tool design, alert fatigue, deployment rules, and recovery delays.

condition: Multiple plausible causal stories compete for the same outcome.

example: A hospital sees readmissions rise and must distinguish discharge instructions, medication access, disease severity, staffing, and community follow-up.

condition: A mechanism is assumed to transfer from one context to another.

example: A training approach that worked in one region is copied elsewhere without checking the incentives, social trust, or infrastructure that made it work.

condition: A high-stakes intervention would be costly, risky, or ethically sensitive if the causal claim is wrong.

example: A regulator considers a ban based on suspected harm and needs to know the causal pathway, evidence strength, and safer intervention points.

symptom: Correlation is treated as explanation

diagnostic_meaning: The team may have observed association but not mapped a mechanism or ruled out confounding.

symptom: Timeline is treated as causation

diagnostic_meaning: The fact that one event came before another is being used as a substitute for mechanism evidence.

symptom: Root cause becomes a slogan

diagnostic_meaning: A named cause is being repeated without showing the chain of conditions and actions through which it produced the effect.

symptom: Interventions repeatedly miss the target

diagnostic_meaning: Actions may be aimed at symptoms, proxies, or politically easy levers rather than causal links.

symptom: Experts agree something matters but disagree on how

diagnostic_meaning: The issue may require explicit mechanism comparison, mediators, and rival explanation testing.

symptom: Evidence is strong for the outcome but weak for the pathway

diagnostic_meaning: The observed effect may be real, but the team cannot yet design reliable intervention because it does not understand the pathway.

root_tension: People need causal explanations to act, but causal certainty is often unavailable. The archetype manages this tension by requiring an explicit, evidence-linked, scope-bounded mechanism that can guide action while remaining open to correction.

condition: The task is only to describe associations without deciding or intervening.

why_not: Use descriptive analytics, evidence inventory, or uncertainty explicitness rather than full causal mechanism mapping.

condition: The mechanism is already well established and the only task is implementation.

why_not: Use implementation design, process control, or operational planning unless context transfer needs renewed causal scrutiny.

condition: The main issue is comparing actual outcome with a plausible alternative path.

why_not: Use Counterfactual Comparison; it may support this archetype but has a different center of gravity.

condition: The main issue is a third-variable distortion of an otherwise clear causal claim.

why_not: Use Confounder Control as the sharper intervention.

condition: The main issue is mapping downstream ripple effects after an action is chosen.

why_not: Use Consequence Cascade Mapping rather than mechanism validation.

condition: The discussion is a philosophical debate about causality without a decision, design, or test implication.

why_not: That may be legitimate theory work, but it is not this solution archetype.

structural_intervention: Turn the causal claim into an inspectable pathway by naming the proposed cause and effect, decomposing the pathway into mechanism links, attaching evidence and uncertainty to each link, checking rival explanations and confounders, identifying where intervention could change the pathway, and defining a test or monitoring plan that can confirm, revise, or reject the map.

State the causal claim

logic: Name the candidate cause, target effect, population or system, and decision that depends on the claim.

Define the outcome precisely

logic: Convert broad outcomes into observable target effects, intermediate signals, and time horizons.

Decompose the mechanism chain

logic: Map the steps by which the cause could produce the effect, including mediators, enabling conditions, constraints, actors, and transfers.

Attach evidence to each link

logic: Record what supports each link and where the chain relies on assumption, analogy, expert judgment, or weak evidence.

name: Check confounders and rival explanations

logic: Ask what else could produce the same relationship and which observations would distinguish the stories.

Set scope and boundary conditions

logic: State where the mechanism is expected to hold, where transfer is uncertain, and what conditions could disable or reverse it.

Select an intervention point

logic: Choose the link or condition where action can plausibly change the pathway with acceptable cost, risk, legitimacy, and observability.

Design a causal test or learning loop

logic: Specify what would change if the mechanism is right, what would falsify or weaken it, and how intermediate signals will be monitored.

Update the map after evidence

logic: Revise the mechanism, scope, or intervention when tests, feedback, failures, or new rival explanations appear.

rule: Do not accept a causal claim for action until the candidate cause, target effect, and at least one plausible mechanism chain are explicit.

reason: This prevents action based solely on association, timing, or authority.

rule: Treat unsupported links as assumptions, not facts.

reason: A partly speculative mechanism may still guide learning, but its status must be visible.

rule: Require at least one rival explanation before high-stakes intervention.

reason: Without rivals, mechanism mapping can become motivated reasoning in diagram form.

rule: Choose intervention points where a change should affect intermediate mechanism signals, not only final outcomes.

reason: Intermediate signals reveal whether the map is working even before final effects are fully visible.

rule: Scope causal claims to conditions under which the mechanism plausibly operates.

reason: Mechanism transfer without boundary conditions creates brittle policy, product, or operational replication.

rule: Escalate to specialized causal inference, ethics review, or domain expertise when stakes, harms, or uncertainty exceed ordinary design judgment.

reason: The archetype is not a license for casual experimentation on people or critical systems.

invariant: Mechanism before intervention

why_it_matters: Action should be tied to how change is expected to happen, not only to observed association.

invariant: Evidence linked to causal links

why_it_matters: The weakest part of the mechanism must be visible enough to test or revise.

invariant: Confounders and rival explanations remain alive until addressed

why_it_matters: Premature closure is one of the main failure modes of causal reasoning.

invariant: Scope and boundary conditions stay explicit

why_it_matters: The map should not claim universality when the evidence only supports a context-bound mechanism.

invariant: Intervention tests inspect intermediate signals as well as outcomes

why_it_matters: Outcome changes alone may not reveal which mechanism produced them.

invariant: Causal mapping does not become blame assignment by default

why_it_matters: Especially in incidents and social systems, explanation should improve intervention, not simply locate a punishable actor.

outcome: More valid causal claims

description: Teams distinguish correlation, sequence, plausibility, and mechanism-supported causation.

outcome: Better-targeted interventions

description: Actions aim at leverage points inside the causal pathway rather than visible symptoms.

outcome: Improved learning from experiments and rollouts

description: Tests examine whether intermediate mechanism signals move as expected, not just whether final metrics changed.

outcome: Reduced blame and story bias

description: Incident and policy explanations become more structural, evidence-linked, and open to revision.

outcome: Safer transfer across contexts

description: Boundary conditions show when a mechanism is likely to hold, fail, or require adaptation.

outcome: Clearer uncertainty and decision rationale

description: Decision-makers can see which causal links are supported, assumed, contested, or still under review.

prose_essence:
Causal Mechanism Mapping is the archetype for situations where people are ready to act on a causal claim but have not yet shown how the proposed cause produces the target effect. It asks for more than an arrow between two variables. It requires a candidate cause, a target effect, a mechanism chain, evidence attached to links in that chain, rival explanations, boundary conditions, an intervention point, and a way to learn whether the map was right.

The practical test is simple: after using this archetype, a team should be able to say, “We think changing this part of the pathway will affect that outcome through these intermediate steps, under these conditions, and we will know we are wrong or incomplete if these signals do not move.”

prose_when_to_use:
Use this archetype when a causal claim is about to guide a decision, intervention, policy, design change, diagnosis, incident response, or evaluation. It is especially useful when a correlation is persuasive but not yet actionable, when a timeline is being treated as proof, when multiple causal stories compete, or when a solution is being copied from one context to another.

It also belongs early in high-stakes work. A public program, medical workflow, safety fix, product intervention, or organizational change can cause harm when the wrong mechanism is assumed. The archetype does not demand perfect certainty before action, but it does require that uncertainty be attached to a mechanism that can be tested and revised.

prose_structural_problem:
The structural problem is causal shortcutting. Actors move from “these things are associated,” “this happened before that,” “this explanation sounds plausible,” or “this actor is visible” to “this caused that.” The missing middle is the mechanism: the pathway through which influence is transmitted.

Without the mechanism, interventions tend to target symptoms, proxies, blamed individuals, or politically convenient levers. A product team may copy a feature without copying the conditions that made it work. An incident team may punish a person without fixing the tool and governance pathway that enabled the failure. A policy team may fund the activity most associated with success while missing selection effects or contextual barriers.

prose_intervention_logic:
The intervention begins by stating the causal claim in bounded terms: what is the candidate cause, what is the target effect, who or what is affected, and what decision depends on the claim? It then decomposes the claim into a mechanism chain. Each link should explain how influence moves from one state to the next.

The map then becomes a review object. Evidence is attached to each link, rival explanations are kept visible, and confounders are checked. The team states boundary conditions so the mechanism does not overclaim universality. Only then does the map select an intervention point: the link or condition where action is expected to change the target effect. The final move is a causal test or learning loop that checks intermediate mechanism signals as well as final outcomes.

prose_key_components:
### Candidate Cause

`candidate_cause` is the part of the archetype that names the proposed causal factor whose influence is being investigated before anyone treats it as an intervention target. The cause remains provisional until the mechanism, evidence, rival explanations, and intervention response are examined. A candidate cause can be an event, condition, decision, behavior, design feature, exposure, or structural factor.

### Target Effect

`target_effect` is the part of the archetype that defines the outcome, behavior, state, failure, or performance change that the candidate cause is supposed to produce or prevent. The target effect must be specific enough to observe or test. Vague effects such as “improves culture” or “causes success” should be decomposed into traceable outcomes before mechanism mapping proceeds.

### Mechanism Chain

`mechanism_chain` is the part of the archetype that connects the candidate cause to the target effect through intermediate steps, mediators, enabling conditions, and transfer points. This is the core component. It prevents the archetype from becoming a correlation label by requiring an explanation of how the effect would occur if the causal claim were true.

### Causal Evidence Record

`causal_evidence_record` is the part of the archetype that collects the evidence supporting each link in the mechanism chain, including observations, experiments, process traces, timing, qualitative accounts, or domain theory. Evidence is attached to links rather than merely to the whole claim. This makes weak links, unsupported leaps, and overconfident causal stories visible.

### Confounder Check

`confounder_check` is the part of the archetype that tests whether a third variable, selection process, common cause, measurement artifact, or omitted context could explain the apparent relationship. Confounder checking is a required guardrail but not the whole archetype. The broader pattern still requires a positive mechanism, an intervention point, and a way to test response.

### Rival Explanation Set

`rival_explanation_set` is the part of the archetype that maintains plausible alternative explanations so the team does not prematurely lock onto the most convenient causal story. Rival explanations may include reverse causation, shared upstream drivers, selection effects, measurement bias, regression to the mean, or multiple interacting causes.

### Causal Claim Scope

`causal_claim_scope` is the part of the archetype that states where, when, for whom, and under what conditions the mapped mechanism is expected to operate. A mechanism that holds in one setting may fail under different constraints, populations, incentives, time horizons, or institutional arrangements. Scope keeps causal claims portable without making them universal.

### Boundary Condition

`boundary_condition` is the part of the archetype that identifies contextual limits or enabling conditions that must be present for the mechanism chain to activate. Boundary conditions prevent a mechanism from becoming a myth of universal causation. They also guide where intervention transfer is safe or unsafe.

### Intervention Point

`intervention_point` is the part of the archetype that selects the link, condition, leverage point, or mediator in the mechanism chain where action is expected to change the target effect. The intervention point turns explanation into design. It should be chosen because the mechanism indicates where change is both possible and causally relevant, not because the point is visible or politically convenient.

### Causal Test

`causal_test` is the part of the archetype that defines how the team will check whether acting on the proposed mechanism produces the expected directional response. The test may be experimental, quasi-experimental, comparative, qualitative process tracing, simulation-based, or staged rollout. The key requirement is that it challenges the mechanism rather than merely records activity.

### Counterfactual Anchor

`counterfactual_anchor` is the part of the archetype that provides a baseline or plausible alternative path against which the mechanism claim and intervention effect can be interpreted. The anchor may be a control group, before/after baseline, matched comparison, synthetic control, or explicitly reasoned alternative. It supports causal interpretation without replacing full Counterfactual Comparison.

### Feedback Monitor

`feedback_monitor` is the part of the archetype that tracks whether post-intervention signals follow the mechanism chain, reveal broken links, or expose unintended effects. Monitoring should inspect intermediate mechanism signals as well as final outcomes; otherwise teams can learn that something changed without learning whether the mapped mechanism was responsible.

prose_common_mechanisms:
Mechanisms are implementation machinery for the archetype. A causal diagram, process-tracing protocol, or root-cause method can help instantiate Causal Mechanism Mapping, but none of them is the archetype by itself. The archetype is the whole intervention pattern: causal claim, mechanism chain, evidence, rivals, scope, intervention point, and learning logic.

### Causal Diagram

`causal_diagram` implements the archetype as a artifact by represents candidate causal links visually so assumptions, missing mediators, feedback paths, and confounders can be inspected together. A diagram can support the archetype, but a diagram alone is not the archetype. It must be tied to evidence, scope, and intervention testing.

### Mechanism Map

`mechanism_map` implements the archetype as a template by breaks a broad causal story into ordered link records, each with an actor, process, evidence basis, uncertainty, and test implication. Useful when the causal pathway is too detailed for a simple diagram or when different teams own different links.

### Root-Cause Analysis

`root_cause_analysis` implements the archetype as a method by investigates upstream contributors to a failure or incident and can instantiate the archetype when it maps how the contributor produced the failure. Root-cause analysis often fails when it names a single root without a mechanism chain. In this draft it is treated as a method, not the parent archetype.

### Causal-Loop Map

`causal_loop_map` implements the archetype as a artifact by shows reinforcing or balancing feedback loops when causal influence cycles through the system rather than moving in a one-way chain. Use when feedback is relevant, while preserving Circular Causality Mapping as a neighboring archetype if loop structure is the main intervention.

### Process Tracing

`process_tracing` implements the archetype as a method by uses sequence evidence, signatures, and within-case observations to test whether the proposed mechanism actually occurred. Common in social science, historical analysis, incident review, policy evaluation, and qualitative causal inference.

### Intervention Test

`intervention_test` implements the archetype as a test_or_assessment by changes a selected intervention point and observes whether intermediate and final outcomes move as predicted by the mechanism. May be an experiment, staged rollout, operational trial, natural experiment, simulation, or careful before/after test depending on context.

### Causal Inference Review

`causal_inference_review` implements the archetype as a procedure by reviews identification assumptions, comparison groups, confounders, and evidence limits before accepting a causal claim. Strong for technical evaluation settings, but the archetype remains broader than statistical identification alone.

### Theory of Change Model

`theory_of_change_model` implements the archetype as a document by documents how activities are expected to produce outputs, outcomes, and impacts through explicit causal pathways. Common in programs, policy, philanthropy, education, and public health; useful when mechanism mapping must guide implementation and evaluation.

### Contribution Analysis

`contribution_analysis` implements the archetype as a method by assesses whether an intervention plausibly contributed to observed outcomes when randomized control or clean isolation is not available. Useful for complex social programs where causal mechanism evidence and alternative explanations must be weighed together.

### Failure Tree Analysis

`failure_tree_analysis` implements the archetype as a artifact by represents combinations of component failures or conditions that can produce an undesired event. Useful in safety and reliability contexts when the target effect is a failure state with multiple enabling conditions.

prose_failure_modes:
A common failure mode is correlation laundering: the team starts with a preferred association and invents a pathway to justify it. The mitigation is to require evidence by link and to keep rival explanations alive.

Another failure mode is diagram authority bias. A polished causal diagram can look more certain than the evidence warrants. The mitigation is to mark uncertainty, unsupported links, and review status on the map.

Single-cause fixation is especially dangerous in incidents. It turns mechanism mapping into blame. The mitigation is to map enabling conditions, controls, constraints, and prevention points.

A mechanism without a test is another failure mode. It may be intellectually satisfying but operationally weak. The mitigation is to require causal tests, intermediate signals, or feedback monitors.

Context overgeneralization occurs when a mechanism that worked in one setting is copied elsewhere without checking boundary conditions. The mitigation is to state scope and transfer assumptions before scaling.

prose_neighbor_distinctions:
**Confounder Control** is a guardrail against third-variable distortion. Causal Mechanism Mapping includes confounder checks, but it also builds the positive pathway and intervention logic.

**Counterfactual Comparison** asks what would likely have happened otherwise. It can help evaluate a mechanism, but it does not by itself map the pathway from cause to effect.

**Hypothesis Testing Frame** structures evidence against defaults and error costs. Causal Mechanism Mapping can generate and test hypotheses, but the center is the causal pathway.

**Consequence Cascade Mapping** explores downstream effects after an action or event. Causal Mechanism Mapping focuses on the mechanism that produces the target effect and where to intervene.

**Circular Causality Mapping** is centered on feedback loops. Causal Mechanism Mapping can include feedback, but if reciprocal loops are the defining structure, the circular-causality neighbor should be considered.

**Root-Cause Analysis** is a mechanism, not the parent archetype. It instantiates this archetype only when it maps the pathway rather than naming a root.

prose_cross_domain_examples:
In public policy, a workforce program may claim to increase employment. Causal Mechanism Mapping distinguishes training quality, credential signaling, employer matching, transportation access, confidence, and labor demand before the program is expanded.

In healthcare, a clinic may believe missed follow-up visits cause readmissions. The map separates discharge comprehension, appointment scheduling, transportation, medication access, symptom escalation, and care coordination.

In product strategy, a team may believe personalization improved retention. The map tests whether the mechanism was onboarding relevance, faster first success, recommendation quality, trust, cohort selection, or a different concurrent change.

In incident analysis, an outage may be attributed to human error. The map asks how deployment tooling, alert design, review gates, time pressure, rollback friction, and escalation rules combined to produce the failure.

In organizational change, leaders may believe meeting-free mornings improve productivity. The map tests whether uninterrupted focus, fewer context switches, clearer asynchronous records, or reduced coordination quality explain the outcome.

prose_non_examples:
A correlation table is not Causal Mechanism Mapping. It may suggest where to look, but it does not specify how influence travels.

A timeline is not Causal Mechanism Mapping. Sequence matters, but earlier events do not automatically cause later ones.

A root-cause slogan is not Causal Mechanism Mapping. Naming “human error,” “culture,” “incentives,” or “leadership” does not show a pathway.

An A/B test without mechanism learning is not Causal Mechanism Mapping. It may support a local decision, but it does not explain why the result occurred or whether it will transfer.

A philosophical debate about causality is not Causal Mechanism Mapping unless it becomes an operational pathway for decision, intervention, and testing.

prose_tradeoffs:
Causal Mechanism Mapping trades speed for causal discipline. It takes longer than naming a cause, but it reduces the risk of acting on a seductive association.

It trades simplicity for explanatory adequacy. A mechanism map can become too complex, but under-mapping can make action brittle.

It trades certainty for structured learning. The map is often provisional; its value comes from making uncertainty inspectable and testable.

It trades portability for context sensitivity. Boundary conditions may make transfer slower, but they prevent naive copy-paste interventions.

prose_invariants_to_preserve:
Preserve mechanism before intervention. A team may act under uncertainty, but it should not pretend a causal pathway is known when only association or sequence is known.

Preserve link-level evidence. The most important uncertainty often hides in a single weak link. A useful map makes weak links visible.

Preserve rival explanations. A mechanism that never faces alternatives easily becomes motivated reasoning.

Preserve boundary conditions. A mechanism is not a universal law simply because it worked once.

Preserve learning. The map should create better tests and monitoring, not just a persuasive story.

Preserve the distinction between explanation and blame. In incidents and social systems, causal mapping should improve prevention and accountability design without collapsing into scapegoating.

prose_target_outcomes:
The target outcome is better causal action. Decisions should be aimed at links that plausibly produce change rather than at symptoms or proxies.

A second outcome is better learning. If an intervention works, the team should learn why. If it fails, the team should see whether the mechanism was wrong, implementation was weak, boundary conditions were absent, or rival explanations were stronger.

A third outcome is safer transfer. When teams adapt a policy, feature, treatment, or operating model to a new context, the map helps them identify which enabling conditions must travel with it.

A fourth outcome is clearer accountability. The archetype can reveal structural contributors, control gaps, and design conditions that a simple blame story would hide.

prose_parameter_dimensions:
The first tuning dimension is **granularity**. A rough mechanism chain may be enough for a low-risk product decision, while a safety, medical, or public policy decision may require link-level evidence, expert review, and explicit ethical safeguards.

The second dimension is **evidence strength**. Some maps are supported by experiments; others rely on process traces, theory, qualitative accounts, or triangulated observational evidence. The map should show the status of each link rather than pretending all evidence is equal.

The third dimension is **time horizon**. Some mechanisms act immediately, while others work through delayed behavior, adaptation, trust, learning, institutional change, or compounding feedback. Time horizon affects what signals should be monitored.

The fourth dimension is **scope of transfer**. A mechanism may hold only for a population, institution, technology stack, culture, market condition, or regulatory environment. The more a mechanism is transferred, the more boundary conditions matter.

The fifth dimension is **ethical and safety sensitivity**. When causal tests affect people or critical systems, the design must account for consent, harm, fairness, privacy, reversibility, and oversight.

prose_variants_and_near_names:
Causal Pathway Mapping emphasizes ordered mediators between cause and effect. Intervention Point Mapping emphasizes choosing the best link to change. Process-Tracing Mechanism Map emphasizes within-case evidence and sequence. Theory-of-Change Mapping translates the archetype into program design and evaluation language. Failure Pathway Mapping specializes the parent for incidents, safety, cybersecurity, and reliability contexts.

Near names such as causal modeling, mechanism mapping, cause-effect mapping, causal diagrams, root-cause analysis, and theory of change should be routed carefully. Some are aliases, some are variants, and some are mechanisms. The key test is whether the named form includes mechanism, evidence, scope, intervention, and learning logic.

Causal Pathway Mapping

variant_type: subtype

short_definition: A variant that emphasizes ordered mediating steps between a cause and an effect.

distinctive_feature: The pathway itself is the main object of analysis.

distinct_from_parent: The parent includes evidence, confounder checks, intervention testing, and scope; this variant stresses pathway decomposition.

still_same_parent_because: It uses the same candidate cause, target effect, mechanism chain, and causal test structure.

use_when: The cause cannot plausibly act directly on the outcome.

use_when: Multiple intermediate states or actors transmit influence.

use_when: The team needs to know which link in the pathway should be strengthened or interrupted.

example_domains: policy healthcare product_strategy

example: Map how tutoring affects attendance, practice, confidence, and mastery.

example: Map how reminders affect appointment completion through attention, scheduling, and transportation planning.

failure_modes: Pathway over-specification without evidence

failure_modes: Ignoring confounders because the pathway feels plausible

Intervention Point Mapping

short_definition: A variant focused on choosing where to act inside a causal mechanism chain.

distinctive_feature: The main decision is selecting a leverage point within the pathway.

distinct_from_parent: The parent maps and tests the whole mechanism; this variant narrows attention to choosing the action point.

still_same_parent_because: The intervention point only makes sense in relation to the causal mechanism map.

use_when: The causal pathway is mostly accepted but the best leverage point is unclear.

use_when: Several links could be changed, but cost, legitimacy, risk, or observability differ.

use_when: Teams are tempted to intervene where it is easiest rather than where the mechanism suggests leverage.

domain: operations

example: Choose whether to fix training, tooling, review gates, or monitoring after mapping a defect pathway.

domain: policy

example: Decide whether to target information, access, incentives, or enforcement in a compliance mechanism.

failure_modes: Choosing visible leverage over causal leverage

failure_modes: Ignoring ethical or legitimacy constraints around intervention

Process-Tracing Mechanism Map

short_definition: A variant that tests mechanism occurrence through within-case sequence evidence and diagnostic observations.

distinctive_feature: It emphasizes traces and sequence tests rather than controlled manipulation.

distinct_from_parent: The parent is mechanism mapping in general; this variant names a qualitative and historical evidence mode.

still_same_parent_because: It still maps cause, mechanism links, evidence, rival explanations, and scope.

use_when: Randomized or clean comparison evidence is unavailable.

use_when: The mechanism should leave observable traces over time.

use_when: Historical, policy, organizational, or incident evidence must be interpreted within a case.

example: Trace whether alert suppression, decision delay, and escalation failure actually occurred before the outage expanded.

example: Trace whether a reform changed agency behavior before outcome improvements appeared.

failure_modes: Cherry-picking traces that fit the story

failure_modes: Treating chronology as diagnostic evidence without discriminating rivals

Theory-of-Change Mapping

short_definition: A program-design variant that maps how activities are expected to produce outputs, outcomes, and impact.

distinctive_feature: It translates causal mechanism mapping into program logic and evaluation planning.

distinct_from_parent: The parent is domain-general; this variant is common in social programs and philanthropic or public-sector evaluation.

still_same_parent_because: Activities, outputs, outcomes, and impacts are a domain-specific vocabulary for candidate cause, mechanism chain, and target effects.

use_when: A program, policy, grant, or initiative needs a shared causal logic.

use_when: Implementation and evaluation need to refer to the same pathway.

use_when: Stakeholders must understand which assumptions connect activities to long-term outcomes.

example_domains: philanthropy education public_health

example: Map how outreach, trust, clinic access, and reminders are expected to increase vaccination.

example: Map how coaching and employer partnerships are expected to improve job placement.

failure_modes: Producing a funder-friendly diagram rather than a testable mechanism

failure_modes: Ignoring assumptions that are politically uncomfortable

Failure Pathway Mapping

short_definition: A variant focused on how conditions combine to produce a failure state and where safeguards can interrupt the path.

distinctive_feature: It frames the mechanism as a preventable pathway to failure.

distinct_from_parent: The parent covers all causal effects; this variant specializes in undesired events and safety controls.

still_same_parent_because: It still uses candidate cause, target effect, mechanism chain, confounder check, intervention point, and causal test.

use_when: The target effect is a safety incident, outage, error, breach, or breakdown.

use_when: The goal is prevention rather than outcome improvement.

use_when: Multiple enabling conditions combine rather than a single cause acting alone.

domain: cybersecurity

example: Map how credential reuse, access scope, vendor permissions, and delayed alerting combine into breach exposure.

domain: safety

example: Map how maintenance delay, sensor noise, operator workload, and procedure ambiguity combine into near misses.

failure_modes: Stopping at the first visible failure rather than mapping enabling conditions

near_names:
Causal Modeling
Mechanism Mapping
Causal Diagram
Root-Cause Analysis
Causal-Loop Mapping
Cause-Effect Mapping
Causal Inference Review
Theory of Change