Machine Learning for Econometrics with Python: Causal Inference, Structural Modeling, and Predictive Methods for Economic Research

By Hayden Van Der Post and Oliver J. Thatch

Reactive Publishing

Modern econometrics is evolving rapidly as machine learning methods reshape how economists analyze complex data. This book provides a rigorous, practical guide to integrating machine learning techniques with the core tools of econometric analysis using Python.

Machine Learning for Econometrics with Python introduces economists, researchers, and quantitative analysts to the growing intersection between statistical learning and economic modeling. The book focuses on how modern machine learning methods can complement traditional econometric frameworks while preserving interpretability, causal reasoning, and structural insight.

Readers will learn how to apply machine learning techniques within the context of real economic research problems, including causal estimation, structural modeling, and high-dimensional prediction.

Topics covered include:

Foundations of machine learning for econometric analysis

Regularization methods such as LASSO and Ridge for economic models

Tree-based methods and ensemble learning for economic forecasting

Causal machine learning approaches including double machine learning and orthogonalization

High-dimensional variable selection in economic datasets

Structural econometric models enhanced with machine learning components

Time-series forecasting using modern machine learning tools

Interpretable machine learning methods for economic research

Simulation and empirical workflows using Python

Throughout the book, practical Python examples demonstrate how machine learning techniques can be implemented using widely adopted scientific libraries such as NumPy, pandas, scikit-learn, and PyTorch.

Rather than replacing econometrics, machine learning expands the economist's toolkit. This book shows how both disciplines can work together to address modern research challenges involving large datasets, complex nonlinear relationships, and high-dimensional economic systems.

Ideal for:

Economists and quantitative researchers

Graduate students in econometrics or applied economics

Data scientists working with economic or financial datasets

Policy analysts interested in modern causal modeling techniques

Machine Learning for Econometrics with Python bridges the gap between statistical learning and economic theory, providing a practical framework for applying machine learning methods to modern econometric research.

• Format: Paperback
• Language: English
• ISBN: B0GSQZ36BQ
• ISBN13: 9798252248318
• Release Date: March 2026
• Publisher: Independently Published
• Length: 588 Pages
• Weight: 1.71 lbs.
• Dimensions: 1.2" x 6.0" x 9.0"