Causal Inference and Treatment Effects Questions

Cover principles and methods for estimating causal effects from experimental and observational data. Topics include the difference between correlation and causation, randomized experiments and A B testing, confounding and identification, propensity score matching, inverse probability weighting, instrumental variables, regression discontinuity, difference in differences, and estimation of heterogeneous treatment effects. Discuss assumptions required for identification, common pitfalls, and how causal estimates are used to inform policy and product interventions.