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

Schwartz, Alice

ISBN 13: 9798252248318

Editore: Independently published, 2026

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Codice articolo 53698847-n

Dati bibliografici

• Titolo: Machine Learning for Econometrics with Python: Causal Inference, Structural Modeling, and Predictive Methods for Economic Research
• Editore: Independently published
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798252248318
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

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.