Machine Learning for Structural Econometrics With Python: A Hands-On Guide to Lasso, Boosting, and Deep IV for Credible Structural Inference

Richman, Grant

ISBN 13: 9798264517730

Published by Independently published, 2025

About this Item

Seller Inventory # 51310523-n

Bibliographic Details

• Title: Machine Learning for Structural Econometrics With Python: A Hands-On Guide to Lasso, Boosting, and Deep IV for Credible Structural Inference
• Publisher: Independently published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798264517730
• Binding: Soft cover
• Condition: New

About this title

Synopsis

The definitive, hands-on path to modern structural econometrics

Built for economists who need results, this book fuses rigor and implementation to deliver structural identification with state-of-the-art machine learning. Across 24 laser-focused chapters, you’ll move from orthogonal moments and cross-fitting to Lasso instrument selection, boosting for conditional moments, and full-blown neural approaches like Deep IV and deep GMM—then stress-test everything with MCQs and end-to-end Python code.

No fluff. No filler. Just the theory you need, followed by immediate self-checks and production-quality implementations for credible, policy-relevant counterfactuals.

Why this book stands out

• Focused and practical: 24 dense chapters each designed to get you from theory to working code fast.
• Inference-first: Orthogonal scores, debiased ML, cross-fitting, and weak-instrument robustness are baked into every workflow.
• Structural credibility: Shape restrictions, moment inequalities, dynamic choices, auctions, platforms, and demand estimation done with ML the right way.
• End-to-end thinking: From identification and tuning to diagnostics, stability checks, and reproducible pipelines.

What you’ll master

• Lasso and post-lasso for instrument and control selection, double selection, and partialling out in high dimensions.
• Boosting for first-stage estimation and overidentified moment systems, with early-stopping as regularization.
• Deep IV and control functions with flexible conditional density estimation (mixture density nets, flows).
• Deep GMM and adversarial moments for conditional moment restrictions.
• Panels and time series with regularization (VAR-lasso, factor-lasso), HAC/cluster-robust inference, and dynamic endogeneity.
• Shape-restricted ML (monotonicity, convexity, homogeneity) for demand systems and game-theoretic models.
• Policy learning and counterfactual evaluation with orthogonal value estimators and robust off-policy tools.

Who this is for

• Graduate students and researchers in economics, public policy, finance, and marketing.
• Quantitative analysts and data scientists moving from prediction to causal and structural analysis.
• Practitioners building decision systems that must withstand scrutiny, replication, and policy stakes.

Get the playbook economists use to deliver credible counterfactuals with modern ML.