Debiased machine learning of conditional average treatment effects and other causal functions

DOI10.1093/ectj/utaa027OpenAlexW3082557888MaRDI QIDQ5083269

Victor Chernozhukov, Vira Semenova

Publication date: 22 June 2022

Published in: The Econometrics Journal (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1702.06240

zbMATH Keywords

machine learningdouble robustnesshigh-dimensional statisticsconditional average treatment effectheterogeneous treatment effectdose-response functionscontinuous treatment effectsdebiased/orthogonal estimationgroup average effects

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (10)

Estimation of partially conditional average treatment effect by double kernel-covariate balancing ⋮ Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence ⋮ Multiply robust estimation of the average treatment effect with missing outcomes ⋮ Debiased machine learning of set-identified linear models ⋮ Optimal Treatment Regimes: A Review and Empirical Comparison ⋮ The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters ⋮ Orthogonal statistical learning ⋮ Analysing a built-in advantage in asymmetric darts contests using causal machine learning ⋮ Model-Assisted Inference for Covariate-Specific Treatment Effects with High-dimensional Data ⋮ Matching on Generalized Propensity Scores with Continuous Exposures

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