Causal Inference & Identification Design

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Abstractions about identifying causal effects from observational data, covering quasi-experimental designs (difference-in-differences, regression discontinuity, natural experiments), identification assumptions (instrumental variables, selection on observables), and evaluation tools like the ROC curve and type M error.

7 abstractions in this family — domain-specific abstractions that sit near one another in structural-signature space (k-means over structural-signature embeddings). Each is shown with its short description.

• Difference-in-Differences — Estimate a causal effect from observational data by subtracting the control group's before-after change from the treatment group's, netting out time-invariant unit confounders and common time trends — valid only if parallel trends holds.
• Instrumental variable — Recover the causal effect of a confounded treatment by finding a quantity Z that moves the treatment, reaches the outcome only through it, and is independent of the confounders — then reading the effect off the ratio of Z's reduced-form to first-stage effects, importing randomization the analyst never performed.
• Natural Experiment — A design that borrows the RCT's identification logic from a real-world process — a policy, boundary, or lottery — judged plausibly as-good-as-random, where the as-if-random assumption must be substantively defended rather than guaranteed by protocol.
• Receiver Operating Characteristic — Sweep a binary classifier's decision threshold across its full score range to trace every achievable sensitivity-versus-false-positive-rate tradeoff at once, factoring detection into orthogonal discriminability (the curve's height) and criterion (where the threshold sits) coordinates.
• Regression Discontinuity Design — Recover a causal effect from a threshold rule by comparing units just above and just below a sharp cutoff on a continuous running variable, where they are comparable in expectation, so any jump in the outcome at exactly the cutoff is attributable to the treatment rather than to selection.
• Selection on Observables — Assume that, conditional on a named set of measured covariates, treatment assignment is independent of potential outcomes — so within each covariate stratum treated and untreated units are exchangeable and adjustment recovers the causal effect.
• Type M Error — Quantify how much a significant effect's reported magnitude is exaggerated by the significance filter under low power, via the exaggeration ratio — the expected significant estimate divided by the true effect — computable from the design before any data exist.