causal-inference-analysislisted

# Causal Inference Analysis Turn a causal claim into an explicit intervention, estimand, identification argument, and falsifiable analysis. Read [references/causal-inference-methods.md](references/causal-inference-methods.md) before choosing a design or adjustment strategy. ## Workflow 1. Define the intervention or exposure, comparator, population, outcome, time zero, follow-up horizon, and target estimand. Reject vague verbs such as “impact” until the counterfactual contrast is clear. 2. Establish temporal ordering and draw the causal assumptions. Distinguish confounders, mediators, colliders, selection mechanisms, measurement error, interference, and time-varying treatment or confounding. 3. Emulate the target experiment conceptually even when only observational data are available: eligibility, assignment strategy, follow-up, outcome, causal contrast, and analysis plan. 4. Select a design whose assumptions fit the data and assignment process. Prefer randomized evidence when ethical and feasible; otherwise justify adjustment, matching, target-trial emulation, difference-in-differences, regression discontinuity, instrumental variables, or another identified strategy rather than choosing by fashion. 5. State the identification assumptions and data requirements before analysis. Never adjust mechanically for every observed variable or condition on a mediator or collider without a causal reason. 6. Check overlap, balance, assignment and expo