Reactive Publishing
Causal inference sits at the center of modern economic research, yet traditional econometric methods increasingly intersect with high-dimensional data and machine learning workflows. Causal Machine Learning for Economists bridges that gap by presenting practical, implementation-focused approaches to contemporary causal modeling using Python.
This book introduces core frameworks including Double Machine Learning (Double ML), Synthetic Control methods, Uplift Modeling, and applied policy evaluation techniques. Rather than treating these topics as isolated tools, the text positions them within a coherent analytical workflow suitable for empirical research, policy analysis, and applied economic modeling.
Readers will learn how to:
Estimate treatment effects using orthogonalized and debiased machine learning approaches
Construct and validate synthetic control models for comparative case analysis
Apply uplift modeling to heterogeneous treatment effect estimation
Implement robust policy evaluation pipelines using modern Python libraries
Integrate causal modeling into reproducible research environments
Each chapter combines conceptual foundations with structured code examples designed for clarity and replicability. Mathematical intuition is presented alongside practical implementation, ensuring that both applied economists and quantitatively trained researchers can operationalize these methods effectively.
This volume is intended for economists, data scientists, graduate students, and policy analysts who seek a structured introduction to causal machine learning within a rigorous economic framework.
