most citedDimension-Free Average Treatment Effect Inference with Deep Neural Networks

2 citations · 2 across the 2 of their papers we have counts for

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2 papers

cs.LG2023

Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features

Hadi Elzayn, Emily Black, Patrick Vossler +3

The vast majority of techniques to train fair models require access to the protected attribute (e.g., race, gender), either at train time or in production. However, in many importa…

stat.ML2021★ 2 cited

Dimension-Free Average Treatment Effect Inference with Deep Neural Networks

Xinze Du, Yingying Fan, Jinchi Lv +2

This paper investigates the estimation and inference of the average treatment effect (ATE) using deep neural networks (DNNs) in the potential outcomes framework. Under some regular…