Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
Nonparametric estimation of causal heterogeneity under high-dimensional confounding
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
This paper considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuous covariates in a selection-on-observables framework where the number of possible confounders is very large. We propose a two-step estimator for which the first step is estimated by machine learning. We show that this estimator has desirable statistical properties like consistency, asymptotic normality and rate double robustness. In particular, we derive the coupled convergence conditions between the nonparametric and the machine learning steps. We also show that estimating population average treatment effects by averaging the estimated heterogeneous effects is semi-parametrically efficient. The new estimator is an empirical example of the effects of mothers' smoking during pregnancy on the resulting birth weight.
years
2026 2representative citing papers
A locally robust, cross-fitted omnibus test detects systematic treatment-effect heterogeneity with respect to low-dimensional covariates while allowing high-dimensional ML-based nuisance estimation across unconfoundedness, DiD, and IV designs.
citing papers explorer
-
Private Rate-Double-Robust Inference
Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
-
A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity
A locally robust, cross-fitted omnibus test detects systematic treatment-effect heterogeneity with respect to low-dimensional covariates while allowing high-dimensional ML-based nuisance estimation across unconfoundedness, DiD, and IV designs.