Applied Causal Inference Questions

Cover practical causal reasoning and methods for decision support. Topics include causal graphs and directed acyclic graphs for modeling assumptions, identification strategies and the required assumptions, methods such as propensity score matching inverse probability weighting instrumental variables regression discontinuity and difference in differences, machine learning based causal methods such as causal forests for heterogeneous treatment effects, sensitivity analysis for unmeasured confounding, diagnostics, and guidance on when to prefer randomized experiments versus observational approaches.