Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges

By Susan Athey, Guido W. Imbens, Thai-Binh Pham, Stefan Wager. 2017

73 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

CausaLM: Causal Model Explanation Through Counterfactual Language Models (2020, background): “The high-dimensional nature of language does not easily fit into the current methods, specifically as the treatment whose effect is being tested is often binary (D’Amour et al. 2017; Athey et al. 2017).”
Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “(2008) and Athey et al. (2018) obtain semiparametric efficiency under strictly weaker conditions than ours on p(x) (the former under minimal smoothness on μt(x) and the latter under a sparsity in a high-dimensional linear model).”
Targeted Undersmoothing. (2017, background): “[6] study estimation of heterogeneous treatment effects in conjunction with machine learning; see also [7].”
The Impact of Machine Learning on Economics (2018, methodology): “The method of Athey et al. (2017e), “generalized random forests,” can be used as an alternative to “traditional” methods such as local generalized method of moments or local maximum likelihood (Tibshirani and Hastie, 1987).”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, methodology): “Other work has explored general methods for adjusting for highdimensional confounders (D’Amour et al., 2017; Rassen et al., 2011; Louizos et al., 2017; Li et al., 2016; Athey et al., 2017).”