Machine Learning for Econometrics with Python: Causal Inference, Structural Modeling, and Predictive Methods for Economic Research by Oliver J. Thatch, Hayden Van Der Post, Alice Schwartz
English | March 15, 2026 | ISBN: N/A | ASIN: B0GSP46FQQ | 619 pages | EPUB | 0.60 Mb
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 analysisRegularization methods such as LASSO and Ridge for economic modelsTree-based methods and ensemble learning for economic forecastingCausal machine learning approaches including double machine learning and orthogonalizationHigh-dimensional variable selection in economic datasetsStructural econometric models enhanced with machine learning componentsTime-series forecasting using modern machine learning toolsInterpretable machine learning methods for economic researchSimulation and empirical workflows using PythonThroughout 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 researchersGraduate students in econometrics or applied economicsData scientists working with economic or financial datasetsPolicy analysts interested in modern causal modeling techniquesMachine 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.
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