Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 0; 2025: 1.
5 papers.
- 2021Estimating Average Treatment Effects via Orthogonal Regularizationcited by 25
- 2022Causal Estimation for Text Data with (Apparent) Overlap Violationscited by 15
- 2021Causal Effect Estimation using Variational Information Bottleneckcited by 4
- 2023Learning sources of variability from high-dimensional observational studiescited by 0
- 2025Structure Maintained Representation Learning Neural Network for Causal Inferencecited by 0
- 2025Structure Maintained Representation Learning Neural Network for Causal Inference3 Aug 2025
- 2023Learning sources of variability from high-dimensional observational studies26 Jul 2023
- 2022Causal Estimation for Text Data with (Apparent) Overlap Violations30 Sep 2022
- 2021Causal Effect Estimation using Variational Information Bottleneck26 Oct 2021
- 2021Estimating Average Treatment Effects via Orthogonal Regularization21 Jan 2021