Skip to content

Missingness Aware Estimator Selection

Choose the missing-data estimator only after stating why values are absent and what assumption makes the target estimand recoverable.

The Diagnostic Story

Symptom: The report notes how many observations were dropped but says nothing about why those observations were missing. Complete-case analysis is the default not because its assumptions are defensible but because it is what the software does. Multiple imputation was applied as a generic repair, but the imputation model omits key predictors. Different analysts, each making defensible choices, produce different conclusions and cannot agree on which one to trust.

Pivot: State the missingness mechanism assumption before choosing an estimator, not after. Inventory which values are absent and why, verify that the identifying condition for the chosen method holds or at least is plausible, and plan sensitivity bounds for the scenarios where the mechanism assumption cannot be justified.

Resolution: Incomplete data are handled in a way that better preserves the intended population and contrast rather than silently substituting a complete-case population. Stakeholders can see which conclusions depend on which mechanism assumptions. False certainty decreases when non-ignorable missingness remains plausible and that possibility is documented alongside the central estimate.

Reach for this when you hear…

[longitudinal health study] “We dropped everyone who missed a follow-up visit, but those people dropped out because they got sicker — that's not random, that's outcome-dependent.”

[market research] “The survey non-responders were exactly the customers most dissatisfied with us — listwise deletion gave us a satisfaction score from our fans.”

[sensor network analysis] “Sensors fail more often in extreme conditions, so the missing readings are precisely the ones that matter most for the safety model.”

Mechanisms / Implementations

  • Doubly Robust Missingness Adjustment
  • Full-Information Maximum Likelihood Path
  • Inverse-Probability Weighting Model
  • MCAR Diagnostic Test and Balance Review
  • Missingness Indicator Matrix
  • Multiple Imputation Workflow
  • Pattern-Mixture Sensitivity Model
  • Process-Based Missingness Audit
  • Selection-Model Sensitivity Analysis
  • Tipping-Point Analysis

Abstractions this archetype builds on — directly (a source ingredient) or as a related pattern. Links follow the typed catalog namespace.

Built directly on (2)

Also references 12 related abstractions

Variants

Narrower or domain-specific specializations that share this archetype's core structure. Recognized variants are established; candidate variants are provisional.

Complete-Case Defensibility Gate · subtype · recognized

A conservative variant that allows complete-case or listwise deletion only when missingness and estimand-change risks are explicitly checked.

MAR-Conditioned Imputation or Weighting · subtype · recognized

A variant that treats missingness as ignorable only conditional on observed covariates and then uses imputation, likelihood, weighting, or hybrid adjustment.

MNAR Sensitivity-Bounded Inference · risk or failure variant · recognized

A variant that treats non-ignorable missingness as unresolved and reports conclusions across explicit sensitivity scenarios rather than pretending the mechanism is harmless.

Longitudinal Attrition Adjustment · domain variant · candidate

A repeated-measurement variant that adjusts for dropout, intermittent missing waves, or follow-up loss while preserving time-dependent estimands.