In recent years, interest in rigorous impact evaluation has grown tremendously in policy-making, economics, public health, social sciences and international relations. Evidence-based policy-making has become a recurring theme in public policy, alongside greater demands for accountability in public policies and public spending, and requests for independent and rigorous impact evaluations for policy evidence. Froelich and Sperlich offer a comprehensive and up-to-date approach to quantitative impact evaluation analysis, also known as causal inference or treatment effect analysis, illustrating the main approaches for identification and estimation: experimental studies, randomization inference and randomized control trials (RCTs), matching and propensity score matching and weighting, instrumental variable estimation, difference-in-differences, regression discontinuity designs, quantile treatment effects, and evaluation of dynamic treatments. The book is designed for economics graduate courses but can also serve as a manual for professionals in research institutes, governments, and international organizations, evaluating the impact of a wide range of public policies in health, environment, transport and economic development.
Impact Evaluation: Treatment Effects and Causal Analysis
1. Basic definitions, assumptions, and randomized experiments; 2. An introduction to nonparametric identification and estimation; 3. Selection on observables: matching, regression and propensity score estimators; 4. Selection on unobservables: nonparametric IV and structural equation approaches; 5. Difference-in-differences estimation: selection on observables and unobservables; 6. Regression discontinuity design; 7. Distributional policy analysis and quantile treatment effects; 8. Dynamic treatment evaluation.