foundations-causal-inference

Causal Inference Foundations

12 applied causal inference primitives for impact attribution and experiment design, backed by a formal theory map. Each primitive solves a specific identification or estimation problem. Primitives are domain-agnostic: the same instrumental-variable logic that handles omitted-variable bias in econometrics handles it in product analytics; the same difference-in-differences framework that evaluates policy interventions evaluates feature rollouts.

When to Apply

Apply causal-inference when:

• "Did the change cause the outcome, or just correlate?" question
• A/B test is impossible (rollout already happened, ethics, ramping risk) — observational methods needed
• Confounding suspected — non-random treatment assignment
• Heterogeneous treatment effects matter (CATE, uplift)
• Mediation question — "is the effect through path X or path Y?"
• Units interfere — marketplace, social graph, shared inventory, ranking model, or agents sharing a backend resource; randomization alone does not identify the launch effect
• LLM evaluation pipeline uses logged data — prompt distribution, judge bias, or user self-selection confound the quality signal (Pearl's Ladder applies: estimating P(Y|do(prompt)) is different from P(Y|prompt))

Skip and use simpler alternatives when:

• Clean RCT / A/B test is already running and units do not interfere — read the result, don't re-derive it observationally. If units share a marketplace, graph, or backend resource, the test is not clean: see Interference and SUTVA
• Question is "how big is the effect?" rather than "does it cause" — descriptive analytics is enough
• No plausible causal mechanism — correlation is just measurement, not insight
• Sample size too small for propensity overlap (n < 1000 typical) — flag and collect more data
• E-value < 1.5 from sensitivity analysis — claim is fragile; do not ship as causal
• Question is about strategic interaction (multi-actor) — use foundations-game-theory