{"product_id":"machine-learning-for-structural-econometrics-grant-richman-9798264517730","title":"Machine Learning for Structural Econometrics With Python: A Hands-On Guide to Lasso, Boosting, and Deep IV for Credible Structural Inference","description":"\u003cb\u003eThe definitive, hands-on path to modern structural econometrics\u003c\/b\u003e\u003cp\u003eBuilt 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.\u003c\/p\u003e\u003cp\u003eNo fluff. No filler. Just the theory you need, followed by immediate self-checks and production-quality implementations for credible, policy-relevant counterfactuals.\u003c\/p\u003e\u003cb\u003eWhy this book stands out\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eFocused and practical: 24 dense chapters each designed to get you from theory to working code fast.\u003c\/li\u003e\n\u003cli\u003eInference-first: Orthogonal scores, debiased ML, cross-fitting, and weak-instrument robustness are baked into every workflow.\u003c\/li\u003e\n\u003cli\u003eStructural credibility: Shape restrictions, moment inequalities, dynamic choices, auctions, platforms, and demand estimation done with ML the right way.\u003c\/li\u003e\n\u003cli\u003eEnd-to-end thinking: From identification and tuning to diagnostics, stability checks, and reproducible pipelines.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eWhat you'll master\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eLasso and post-lasso for instrument and control selection, double selection, and partialling out in high dimensions.\u003c\/li\u003e\n\u003cli\u003eBoosting for first-stage estimation and overidentified moment systems, with early-stopping as regularization.\u003c\/li\u003e\n\u003cli\u003eDeep IV and control functions with flexible conditional density estimation (mixture density nets, flows).\u003c\/li\u003e\n\u003cli\u003eDeep GMM and adversarial moments for conditional moment restrictions.\u003c\/li\u003e\n\u003cli\u003ePanels and time series with regularization (VAR-lasso, factor-lasso), HAC\/cluster-robust inference, and dynamic endogeneity.\u003c\/li\u003e\n\u003cli\u003eShape-restricted ML (monotonicity, convexity, homogeneity) for demand systems and game-theoretic models.\u003c\/li\u003e\n\u003cli\u003ePolicy learning and counterfactual evaluation with orthogonal value estimators and robust off-policy tools.\u003c\/li\u003e\n\u003c\/ul\u003eWho this is for\u003cul\u003e\n\u003cli\u003eGraduate students and researchers in economics, public policy, finance, and marketing.\u003c\/li\u003e\n\u003cli\u003eQuantitative analysts and data scientists moving from prediction to causal and structural analysis.\u003c\/li\u003e\n\u003cli\u003ePractitioners building decision systems that must withstand scrutiny, replication, and policy stakes.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003c\/p\u003e\u003cb\u003eGet the playbook economists use to deliver credible counterfactuals with modern ML.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Grant Richman\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798264517730\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/09\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 380\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.12lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.78d","brand":"Grant Richman","offers":[{"title":"Paperback","offer_id":47931108229375,"sku":"9798264517730","price":39.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_2dca3b5c-ee26-408d-b9f7-1b561211ed13.jpg?v=1766346504","url":"https:\/\/www.whiterainbookhouse.com\/products\/machine-learning-for-structural-econometrics-grant-richman-9798264517730","provider":"WR Book House","version":"1.0","type":"link"}