Tomu Hirata, Undral Byambadalai, Tatsushi Oka, Shota Yasui, Shingo Uto
arXiv 10 Jul 2025 · Machine Learning · 1 citations (OpenAlex)
arXiv:2507.07738 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel multi-task neural network approach for estimating distributional treatment effects (DTE) in randomized experiments. While DTE provides more granular insights into the experiment outcomes over conventional methods focusing on the Average Treatment Effect (ATE), estimating it with regression adjustment methods presents significant challenges. Specifically, precision in the distribution tails suffers due to data imbalance, and computational inefficiencies arise from the need to solve numerous regression problems, particularly in large-scale datasets commonly encountered in industry. To address these limitations, our method leverages multi-task neural networks to estimate conditional outcome distributions while incorporating monotonic shape constraints and multi-threshold label learning to enhance accuracy. To demonstrate the practical effectiveness of our proposed method, we apply our method to both simulated and real-world datasets, including a randomized field experiment aimed at reducing water consumption in the US and a large-scale A/B test from a leading streaming platform in Japan. The experimental results consistently demonstrate superior performance across various datasets, establishing our method as a robust and practical solution for modern causal inference applications requiring a detailed understanding of treatment effect heterogeneity.
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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 | Alberto Abadie (2002) Bootstrap tests for distributional treatment effects in instrumental variable models | 0.644 | 2 | 2 | 100% |
| 2 | Susan Athey and Guido W. Imbens (2006) Identification and inference in nonlinear difference-in-differences models | 0.644 | 2 | 2 | 100% |
| 3 | Victor Chernozhukov and Christian Hansen (2005) An iv model of quantile treatment effects | 0.644 | 2 | 2 | 100% |
| 4 | Denis Chetverikov, Andres Santos, and Azeem M Shaikh (2018) The econometrics of shape restrictions | 0.644 | 2 | 2 | 100% |
| 5 | Guido W. Imbens and Donald B. Rubin (2015) Causal Inference for Statistics, Social, and Biomedical Sciences | 0.644 | 2 | 2 | 100% |
| 6 | Michael Crawshaw (2020) Multi-task learning with deep neural networks: A survey | 0.644 | 2 | 2 | 100% |
| 7 | Donald B Rubin (1974) Estimating causal effects of treatments in randomized and nonrandomized studies | 0.644 | 2 | 2 | 100% |
| 8 | Donald B Rubin (1980) Randomization analysis of experimental data: The fisher randomization test comment, 1980 | 0.644 | 2 | 2 | 100% |
| 9 | Seungil You, David Ding, Kevin Canini, Jan Pfeifer, and Maya Gupta (2017) Deep lattice networks and partial monotonic functions | 0.644 | 2 | 2 | 100% |
| 10 | Maya Gupta, Dara Bahri, Andrew Cotter, and Kevin Canini (2018) Diminishing returns shape constraints for interpretability and regularization | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 69 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Beyond the Average: Distributional Causal Inference under Imperfect Compliance | 0.405 | 1 | 1 |