causal-inference-skill-for-marketing

Use when the user needs augmented inverse probability weighting (AIPW) or doubly robust (DR) estimation for causal inference from observational data. AIPW combines an outcome regression model with propensity score weighting so the estimator is consistent if EITHER the propensity model OR the outcome model is correctly specified. Covers ATE and ATT estimation, the efficient influence function and its use for variance and confidence intervals, cross-fitting / sample splitting (TMLE-adjacent and double machine learning, DML, ideas) to control overfitting when using flexible ML learners, robustness to model misspecification, overlap/positivity checks, and clear reporting for treatment effect studies in Python, R, or SQL-backed analytics workflows.

Use as the entry point for any causal inference or treatment-effect question when the right method is not yet decided. This router inspects the user's data shape (cross-sectional vs panel, randomized vs observational, sample size) and business goal (average effect vs who-to-target), then recommends and dispatches to the appropriate sub-skill — PSM (psm-causal-inference), IPW (ipw-causal-inference), AIPW/doubly-robust (aipw-causal-inference), DID (did-causal-inference), or Uplift modeling (uplift-modeling). Triggers include 评估某个动作/投放/政策的因果效果, incrementality, lift, treatment effect, ATE, ATT, CATE, 该对谁投放 (who-to-target), A/B test analysis with confounding, observational treatment-effect studies, policy evaluation, and questions like 用哪种因果推断方法 / which causal method should I use. Prefer this skill first whenever the user uploads data and asks for a causal effect without naming a specific method.

Use when the user needs difference-in-differences (DID) for causal inference, including DID treatment effect estimation, the parallel trends assumption, two-period/two-group (2x2) designs, policy or intervention/program evaluation, natural experiments, event studies for dynamic effects, two-way fixed effects (TWFE) regressions, pre-trend checks, staggered adoption caveats (Goodman-Bacon, Callaway-Sant'Anna, Sun-Abraham), ATT estimation, and clear reporting for panel data or repeated cross-section treatment-effect studies in Python, R, Stata, or SQL-backed analytics workflows.

Use when the user needs inverse probability weighting (IPW) or inverse probability of treatment weighting (IPTW) for causal inference from observational data, including propensity-score weighting, stabilized weights, ATE or ATT estimation via weighting, an introduction to marginal structural models, positivity/overlap diagnostics, weight trimming and truncation, effective sample size, Horvitz-Thompson and Hajek estimators, and covariate balance after weighting using weighted standardized mean differences. Applies to Python, R, Stata, or SQL-backed analytics workflows for observational treatment effect studies.

Use when the user needs propensity score methods for causal inference, including propensity score matching (PSM), inverse probability weighting (IPW), overlap/common support checks, caliper or nearest-neighbor matching, covariate balance diagnostics using standardized mean differences, ATT/ATE estimation, sensitivity analysis, and clear reporting for observational treatment effect studies in Python, R, Stata, or SQL-backed analytics workflows.

Use when the user needs uplift modeling to estimate individual or conditional treatment effects (CATE/ITE) and heterogeneous treatment effects, i.e. who-to-target / treatment targeting questions rather than a single average effect. Covers segmenting a population into persuadables, sure things, lost causes, and sleeping dogs; meta-learners (S-learner, T-learner, X-learner) and uplift/causal trees; ranking-based evaluation with the Qini curve, uplift curve, and AUUC/Qini coefficient instead of ordinary classification or regression metrics; and marketing campaign targeting, retention, and budget allocation. Best suited to settings with a treatment flag and an outcome, ideally from a randomized A/B test or quasi-random/de-confounded assignment, where the goal is individual-level heterogeneity. Applies to Python, R, or SQL-backed analytics workflows.