Causal Inference and Machine Learning

We wrote this book to fill a real gap: a single, coherent guide that connects econometrics, causal methods grounded in the counterfactual framework, and modern machine learning in the settings you work in. Many resources are either too technical or assume a background that beginners don’t yet have. We take a rigorous but approachable path: early chapters slow down for the essentials—estimation vs. prediction, the bias–variance trade-off, overfitting, tuning, and validation—then build toward the most up-to-date prediction and causal estimation methods you’ll use in practice.