arXiv 18 Oct 2025 · Econometrics · publishedEconometrics Journal (2026)
arXiv:2510.16669 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a debiased estimator for causal effects in high-dimensional generalized linear models with binary outcomes and general link functions. The estimator augments a regularized regression plug-in with weights computed from a convex optimization problem that approximately balances link-derivative-weighted covariates and controls variance; it does not rely on estimated propensity scores. Under standard conditions, the estimator is $\sqrt{n}$-consistent and asymptotically normal for dense linear contrasts and causal parameters. Simulation results show the superior performance of our approach in comparison to alternatives such as inverse propensity score estimators and double machine learning estimators in finite samples. In an application to the National Supported Work training data, our estimates and confidence intervals are close to the experimental benchmark.
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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 | Cai, T. T., Z. Guo, and R. Ma (2023) Statistical inference for high-dimensional generalized linear models with binary outcomes | 0.928 | 10 | 5 | 80% |
| 2 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.843 | 4 | 4 | 75% |
| 3 | Athey, S., G. W. Imbens, and S. Wager (2018) Approximate residual balancing: Debiased inference of average treatment effects in high dimensions | 0.794 | 6 | 4 | 50% |
| 4 | Belloni, A., V. Chernozhukov, and Y. Wei (2016) Post-selection inference for generalized linear models with many controls | 0.737 | 3 | 3 | 67% |
| 5 | Chernozhukov, V., W. K. Newey, and R. Singh (2022) Automatic debiased machine learning of causal and structural effects | 0.737 | 3 | 3 | 67% |
| 6 | Belloni, A., V. Chernozhukov, I. Fernandez-Val, and C. Hansen (2017) Program evaluation and causal inference with high-dimensional data | 0.644 | 2 | 2 | 100% |
| 7 | Farrell, M. H (2015) Robust inference on average treatment effects with possibly more covariates than observations | 0.644 | 2 | 2 | 100% |
| 8 | Hirshberg, D. A. and S. Wager (2021) Augmented minimax linear estimation | 0.644 | 2 | 2 | 100% |
| 9 | Van de Geer, S., P. Bühlmann, Y. Ritov, and R. Dezeure (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 0.644 | 2 | 2 | 100% |
| 10 | Dehejia, R. H. and S. Wahba (1999) Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 26 scored citations.