Jelena Bradic, Stefan Wager, Yinchu Zhu
arXiv 2 May 2019 · Mathematics — Statistics Theory · 6 citations (OpenAlex)
arXiv:1905.00744 · PDF · DOI · OpenAlex · Extracted main text
Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to validate in practice. To alleviate this difficulty, we here study a new method for average treatment effect estimation that yields asymptotically exact confidence intervals assuming that either the conditional response surface or the conditional probability of treatment allows for an ultra-sparse representation (but not necessarily both). This guarantee allows us to provide valid inference for average treatment effect in high dimensions under considerably more generality than available baselines. In addition, we showcase that our results are semi-parametrically efficient.
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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 | Susan Athey, Guido W Imbens, and Stefan Wager (2018) Approximate residual balancing: Debiased inference of average treatment effects in high dimensions self | 1.000 | 6 | 4 | 100% |
| 2 | Zhiqiang Tan (2019) Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data | 1.000 | 5 | 3 | 100% |
| 3 | Victor Chernozhukov, Whitney Newey, and James Robins (1802) Double/de-biased machine learning using regularized Riesz representers | 0.874 | 7 | 2 | 100% |
| 4 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 5 | 2 | 100% |
| 5 | Whitney K Newey and James R Robins (2018) Cross-fitting and fast remainder rates for semiparametric estimation | 0.843 | 3 | 3 | 100% |
| 6 | Max H Farrell (2015) Robust inference on average treatment effects with possibly more covariates than observations | 0.737 | 3 | 2 | 100% |
| 7 | Yang Ning, Sida Peng, and Kosuke Imai (2018) Robust estimation of causal effects via high-dimensional covariate balancing propensity score | 0.737 | 3 | 2 | 100% |
| 8 | James M Robins, Andrea Rotnitzky, and Lue Ping Zhao (1994) Estimation of regression coefficients when some regressors are not always observed | 0.737 | 3 | 2 | 100% |
| 9 | José R Zubizarreta (2015) Stable weights that balance covariates for estimation with incomplete outcome data | 0.737 | 3 | 2 | 100% |
| 10 | Alexandre Belloni, Victor Chernozhukov, and Christian Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 0.644 | 2 | 2 | 100% |
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