Impact Evaluation in Firms and Organizations

1 Introduction 2 Basics of impact evaluation 2.1 The fundamental problem of impact evaluation 2.2 Analyzing the impact: characterization and assessment 2.3 The problem of comparing apples to oranges 3 Experiments (A/B testing) 3.1 Comparing apples to apples 3.2 Behavioral assumptions and methods for analyzing experiments 3.3 Multiple interventions 3.4 Use cases in R 3.5 Use cases in Python 4 Selection on observables: aim to compare apples with apples 4.1 Making groups comparable in observed characteristics 4.2 Behavioral assumptions 4.3 Methods for impact evaluation 4.4 Use cases in R 4.5 Use cases in Python 5 Causal machine learning 5.1 Motivating causal machine learning 5.2 Elements of causal machine learning 5.3 A brief introduction to several machine learning algorithms 5.4 Effect heterogeneity and optimal policy learning 5.5 Use cases in R 5.6 Use cases in Python 6 Instrumental variables 6.1 Instruments and complier effects 6.2 Behavioral assumptions 6.3 Use cases in R 7 Use cases in Python 8 Regression discontinuity designs 8.1 Sharp and fuzzy regression discontinuity designs 8.2 Behavioral assumptions and methods 8.3 Use cases in R 8.4 Use cases in Python 9 Difference-in-Differences 9.1 Difference-in-Differences and the impact in the treatment group 9.2 Behavioral assumptions and extensions 9.3 Use cases in R 9.4 Use cases in Python 10 Synthetic controls 10.1 Impact evaluation when a single unit receives the intervention 10.2 Behavioral assumptions and variants 10.3 Use cases in R 11 Use cases in Python 12 Conclusion