# Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges

arXiv: https://arxiv.org/abs/1702.01250 · HTML: https://mlsanity.com/paper/1702.01250/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2017.

## Built on by (most cited first, 5 of 5)

- [Estimating Average Treatment Effects via Orthogonal Regularization](https://mlsanity.com/paper/2101.08490/) (2021)
- [Causal Estimation for Text Data with (Apparent) Overlap Violations](https://mlsanity.com/paper/2210.00079/) (2022)
- [Causal Effect Estimation using Variational Information Bottleneck](https://mlsanity.com/paper/2110.13705/) (2021)
- [Learning sources of variability from high-dimensional observational studies](https://mlsanity.com/paper/2307.13868/) (2023)
- [Structure Maintained Representation Learning Neural Network for Causal Inference](https://mlsanity.com/paper/2508.01865/) (2025)

## Abstract (arXiv)

There is a large literature on semiparametric estimation of average treatment effects under unconfounded treatment assignment in settings with a fixed number of covariates. More recently attention has focused on settings with a large number of covariates. In this paper we extend lessons from the earlier literature to this new setting. We propose that in addition to reporting point estimates and standard errors, researchers report results from a number of supplementary analyses to assist in assessing the credibility of their estimates.