Causal Inference and Confounding Questions

Foundational concepts and methods for reasoning about cause and effect and for estimating causal effects from experimental and observational data. Topics include the distinction between correlation and causation, causal graphs and directed acyclic graphs, sources of confounding bias, randomized experiments, instrumental variable approaches, difference in differences, regression discontinuity designs, propensity score methods, sensitivity analysis, diagnostics for assumptions, and considerations for external validity and transportability.