arXiv 11 Nov 2025 · Statistics — Machine Learning
arXiv:2511.08303 · PDF · DOI · OpenAlex · Extracted main text
This study investigates treatment effect estimation in the semi-supervised setting, where we can use not only the standard triple of covariates, treatment indicator, and outcome, but also unlabeled auxiliary covariates. For this problem, we develop efficiency bounds and efficient estimators whose asymptotic variance aligns with the efficiency bound. In the analysis, we introduce two different data-generating processes: the one-sample setting and the two-sample setting. The one-sample setting considers the case where we can observe treatment indicators and outcomes for a part of the dataset, which is also called the censoring setting. In contrast, the two-sample setting considers two independent datasets with labeled and unlabeled data, which is also called the case-control setting or the stratified setting. In both settings, we find that by incorporating auxiliary covariates, we can lower the efficiency bound and obtain an estimator with an asymptotic variance smaller than that without such auxiliary covariates.
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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 | Masahiro Kato (2026) A unified framework for debiased machine learning: Riesz representer fitting under bregman divergence, 2026a self | 0.928 | 10 | 6 | 80% |
| 2 | Masatoshi Uehara, Masahiro Kato, and Shota Yasui (2020) Off-policy evaluation and learning for external validity under a covariate shift self | 0.928 | 5 | 4 | 80% |
| 3 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 5 | 3 | 80% |
| 4 | Masahiro Kato (2025) Direct bias-correction term estimation for propensity scores and average treatment effect estimation, 2025a self | 0.894 | 7 | 3 | 71% |
| 5 | Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, and… (2021) Automatic debiased machine learning via riesz regression, 2021 | 0.874 | 5 | 2 | 100% |
| 6 | Masahiro Kato (2025) Direct debiased machine learning via bregman divergence minimization, 2025b self | 0.747 | 12 | 3 | 42% |
| 7 | Aad W. van der Vaart (1998) Asymptotic Statistics | 0.737 | 4 | 2 | 75% |
| 8 | Jinyong Hahn (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects | 0.737 | 3 | 3 | 67% |
| 9 | Guido W. Imbens and Tony Lancaster (1996) Efficient estimation and stratified sampling | 0.644 | 2 | 2 | 100% |
| 10 | Masanori Kawakita and Takafumi Kanamori (2013) Semi-supervised learning with density-ratio estimation | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 55 scored citations.