Jann Spiess, Vasilis Syrgkanis, Victor Yaneng Wang
arXiv 12 Mar 2021 · Econometrics
arXiv:2103.07066 · PDF · DOI · OpenAlex · Extracted main text
Researchers often run resource-intensive randomized controlled trials (RCTs) to estimate the causal effects of interventions on outcomes of interest. Yet these outcomes are often noisy, and estimated overall effects can be small or imprecise. Nevertheless, we may still be able to produce reliable evidence of the efficacy of an intervention by finding subgroups with significant effects. In this paper, we propose a machine-learning method that is specifically optimized for finding such subgroups in noisy data. Unlike available methods for personalized treatment assignment, our tool is fundamentally designed to take significance testing into account: it produces a subgroup that is chosen to maximize the probability of obtaining a statistically significant positive treatment effect. We provide a computationally efficient implementation using decision trees and demonstrate its gain over selecting subgroups based on positive (estimated) treatment effects. Compared to standard tree-based regression and classification tools, this approach tends to yield higher power in detecting subgroups affected by the treatment.
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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 | Foster, Dylan J and Vasilis Syrgkanis (2019) Orthogonal statistical learning | 0.928 | 4 | 4 | 100% |
| 2 | Armstrong, Timothy B and Shu Shen (2015) Inference on optimal treatment assignments | 0.737 | 3 | 2 | 100% |
| 3 | Athey, Susan and Guido Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.737 | 3 | 2 | 100% |
| 4 | Nie, Xinkun and Stefan Wager (2017) Quasi-oracle estimation of heterogeneous treatment effects | 0.737 | 3 | 2 | 100% |
| 5 | Syrgkanis, Vasilis and Manolis Zampetakis (2020) Estimation and inference with trees and forests in high dimensions self | 0.693 | 8 | 2 | 50% |
| 6 | Chernozhukov, Victor, Whitney Newey, and James Robins (2018) Double/de-biased machine learning using regularized riesz representers | 0.644 | 2 | 2 | 100% |
| 7 | Dudḱ, Miroslav, John Langford, and Lihong Li (2011) Doubly robust policy evaluation and learning | 0.644 | 2 | 2 | 100% |
| 8 | Oprescu, Miruna, Vasilis Syrgkanis, and Zhiwei Steven Wu (2019) Orthogonal random forest for causal inference self | 0.644 | 2 | 2 | 100% |
| 9 | Swaminathan, Adith and Thorsten Joachims (2015) Counterfactual risk minimization: Learning from logged bandit feedback | 0.644 | 2 | 2 | 100% |
| 10 | Zhou, Zhengyuan, Susan Athey, and Stefan Wager (2018) Offline multi-action policy learning: Generalization and optimization | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery | 0.511 | 2 | 1 |