The Data Analyst's Guide to Cause and Effect : An Introduction to Causal Inference in Practice (Quantitative Applications in the Social Sciences)

The Data Analyst's Guide to Cause and Effect offers a clear, practical roadmap built around the EEESI workflow—Estimand, Estimator, Estimate, Simulation-based Inference. This book provides a systematic approach to defining, estimating, and validating causal effects, allowing readers to learn to apply modern techniques and move beyond simple associations to make credible causal inferences that inform theory, policy, and practice.