Double/debiased ML framework for average derivative effects in panel data with continuous treatments, two-way fixed effects, and endogeneity.
Newey, Victor Quintas-Martinez, and Vasilis Syrgkanis
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DOPE is a Neyman-orthogonal one-step semiparametric estimator that removes first-order bias in functional estimates from neural operators by learning weights via Riesz regression.
Incorporating unlabeled auxiliary covariates lowers the efficiency bound for treatment effect estimation and produces estimators with smaller asymptotic variance than those without the auxiliary data.
Balancing in debiased machine learning for causal effects should be guided by the Neyman orthogonal score, with covariate balancing as a special case appropriate only when regression errors depend solely on covariates.
citing papers explorer
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Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity
Double/debiased ML framework for average derivative effects in panel data with continuous treatments, two-way fixed effects, and endogeneity.
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Debiased neural operators for estimating functionals
DOPE is a Neyman-orthogonal one-step semiparametric estimator that removes first-order bias in functional estimates from neural operators by learning weights via Riesz regression.
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Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
Incorporating unlabeled auxiliary covariates lowers the efficiency bound for treatment effect estimation and produces estimators with smaller asymptotic variance than those without the auxiliary data.
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Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
Balancing in debiased machine learning for causal effects should be guided by the Neyman orthogonal score, with covariate balancing as a special case appropriate only when regression errors depend solely on covariates.