The second edition of the Impact Evaluation in Practice handbook is a comprehensive and accessible introduction to impact evaluation for policy makers and devel

This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is

CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simp