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

Name: Machine Learning for Econometrics with Python: Causal Inference, Structural Modeling, and Predictive Methods for Economic Research
Brand: Independently Published
SKU: 9798252248318
Price: 3127.0 INR
Availability: InStock

by Alice Schwartz , Hayden Van Der Post , Oliver J. Thatch

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ISBN13: 9798252248318
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: March 15, 2026
Pages: 588
Original Price: GBP 27.22
Language: English
Edition: N/A
Item Weight: 776 grams
BISAC Subject(s): Econometrics

About The Book

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.

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