Susan Athey, Guido W. Imbens, Stefan Wager
arXiv 25 Apr 2016 · Statistics — Methodology · 6 citations (OpenAlex)
arXiv:1604.07125 · PDF · DOI · OpenAlex · Extracted main text
There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on pre-treatment variables. The unconfoundedness assumption is often more plausible if a large number of pre-treatment variables are included in the analysis, but this can worsen the performance of standard approaches to treatment effect estimation. In this paper, we develop a method for de-biasing penalized regression adjustments to allow sparse regression methods like the lasso to be used for sqrt{n}-consistent inference of average treatment effects in high-dimensional linear models. Given linearity, we do not need to assume that the treatment propensities are estimable, or that the average treatment effect is a sparse contrast of the outcome model parameters. Rather, in addition standard assumptions used to make lasso regression on the outcome model consistent under 1-norm error, we only require overlap, i.e., that the propensity score be uniformly bounded away from 0 and 1. Procedurally, our method combines balancing weights with a regularized regression adjustment.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | A. Belloni, V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 1.000 | 14 | 5 | 100% |
| 2 | J. R. Zubizarreta (2015) Stable weights that balance covariates for estimation with incomplete outcome data | 1.000 | 11 | 3 | 100% |
| 3 | M. H. Farrell (2015) Robust inference on average treatment effects with possibly more covariates than observations | 1.000 | 9 | 4 | 100% |
| 4 | A. Belloni, V. Chernozhukov, I. Fernández-Val, and C. Hansen (2017) Program evaluation and causal inference with high-dimensional data | 1.000 | 8 | 4 | 100% |
| 5 | J. M. Robins, A. Rotnitzky, and L. P. Zhao (1994) Estimation of regression coefficients when some regressors are not always observed | 1.000 | 6 | 4 | 100% |
| 6 | M. J. Van Der Laan and D. Rubin (2006) Targeted maximum likelihood learning | 0.928 | 4 | 4 | 100% |
| 7 | J. Kang and J. Schafer (2007) Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data | 0.928 | 4 | 3 | 100% |
| 8 | J. Hainmueller (2012) Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies | 0.874 | 8 | 2 | 100% |
| 9 | A. Javanmard and A. Montanari (2014) Confidence intervals and hypothesis testing for high-dimensional regression | 0.874 | 8 | 2 | 100% |
| 10 | B. Graham, C. Pinto, and D. Egel (2012) Inverse probability tilting for moment condition models with missing data | 0.874 | 7 | 2 | 100% |
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