Mingqian Guan, Komei Fujita, Naoya Sueishi, Shota Yasui
arXiv 12 Oct 2025 · Econometrics
arXiv:2510.10527 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a new method for estimating conditional average treatment effects (CATE) in randomized experiments. We adopt inverse probability weighting (IPW) for identification; however, IPW-transformed outcomes are known to be noisy, even when true propensity scores are used. To address this issue, we introduce a noise reduction procedure and estimate a linear CATE model using Lasso, achieving both accuracy and interpretability. We theoretically show that denoising reduces the prediction error of the Lasso. The method is particularly effective when treatment effects are small relative to the variability of outcomes, which is often the case in empirical applications. Applications to the Get-Out-the-Vote dataset and Criteo Uplift Modeling dataset demonstrate that our method outperforms fully nonparametric machine learning methods in identifying individuals with higher treatment effects. Moreover, our method uncovers informative heterogeneity patterns that are consistent with previous empirical findings.
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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 | Kennedy, Edward H (2023) Towards optimal doubly robust estimation of heterogeneous causal effects | 0.928 | 4 | 3 | 100% |
| 2 | Gerber, Alan S, Green, Donald P, Larimer, Christopher W (2008) Social pressure and voter turnout: Evidence from a large-scale field experiment | 0.811 | 4 | 2 | 100% |
| 3 | Künzel, Sören R, Sekhon, Jasjeet S, Bickel, Peter J, Yu, Bin (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.737 | 3 | 2 | 100% |
| 4 | Nie, Xinkun, Wager, Stefan (2021) Quasi-oracle estimation of heterogeneous treatment effects | 0.737 | 3 | 2 | 100% |
| 5 | Bühlmann, Peter (2011) Statistics for high-dimensional data: methods, theory and applications | 0.644 | 4 | 1 | 100% |
| 6 | Athey, Susan, Keleher, Niall, Spiess, Jann (2025) Machine learning who to nudge: causal vs predictive targeting in a field experiment on student financial aid renewal | 0.644 | 2 | 2 | 100% |
| 7 | Diemert, Eustache, Betlei, Artem, Renaudin, Christophe, Amini, Massi… (2018) A large scale benchmark for uplift modeling | 0.644 | 2 | 2 | 100% |
| 8 | Gutierrez, Pierre, Gérardy, Jean-Yves (2017) Causal inference and uplift modelling: A review of the literature | 0.644 | 2 | 2 | 100% |
| 9 | Wager, Stefan, Athey, Susan (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.644 | 2 | 2 | 100% |
| 10 | Bühlmann, Peter (2015) High-dimensional inference in misspecified linear models | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 41 scored citations.