Develops DML-PPCI estimators (EE and TMLE variants) that attain the semiparametric efficiency bound derived for semi-supervised causal inference via semi-supervised generalized Riesz regression.
arXiv preprint arXiv:2104.14737 , year=
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
Double/debiased ML framework for average derivative effects in panel data with continuous treatments, two-way fixed effects, and endogeneity.
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
-
Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression
Develops DML-PPCI estimators (EE and TMLE variants) that attain the semiparametric efficiency bound derived for semi-supervised causal inference via semi-supervised generalized Riesz regression.
-
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.
-
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.
-
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.
-
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.