arXiv 12 Nov 2020 · Econometrics · publishedManagement Science (2024) · 6 citations (OpenAlex)
arXiv:2011.06528 · PDF · DOI · OpenAlex · Extracted main text
There is increasing interest in allocating treatments based on observed individual characteristics: examples include targeted marketing, individualized credit offers, and heterogeneous pricing. Treatment personalization introduces incentives for individuals to modify their behavior to obtain a better treatment. Strategic behavior shifts the joint distribution of covariates and potential outcomes. The optimal rule without strategic behavior allocates treatments only to those with a positive Conditional Average Treatment Effect. With strategic behavior, we show that the optimal rule can involve randomization, allocating treatments with less than 100% probability even to those who respond positively on average to the treatment. We propose a sequential experiment based on Bayesian Optimization that converges to the optimal treatment rule without parametric assumptions on individual strategic behavior.
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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 | Alex Frankel and Navin Kartik (2022) Improving information from manipulable data | 0.928 | 4 | 3 | 100% |
| 2 | Charles F Manski (2004) Statistical treatment rules for heterogeneous populations | 0.874 | 7 | 2 | 100% |
| 3 | Benjamin Letham, Brian Karrer, Guilherme Ottoni, and Eytan Bakshy (2019) Constrained bayesian optimization with noisy experiments | 0.811 | 4 | 2 | 100% |
| 4 | Christopher Adjaho and Timothy Christensen (2022) Externally valid treatment choice | 0.737 | 3 | 2 | 100% |
| 5 | Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, and Keziah Naggita (2021) The strategic perceptron | 0.737 | 3 | 2 | 100% |
| 6 | Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, and Zhiwei… (2018) Strategic classification from revealed preferences | 0.737 | 3 | 2 | 100% |
| 7 | Nathan Kallus and Angela Zhou (2021) Minimax-optimal policy learning under unobserved confounding | 0.737 | 3 | 2 | 100% |
| 8 | Jasper Snoek, Hugo Larochelle, and Ryan P Adams (2012) Practical bayesian optimization of machine learning algorithms | 0.737 | 3 | 2 | 100% |
| 9 | Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger (2010) Gaussian process optimization in the bandit setting: No regret and experimental design | 0.644 | 4 | 1 | 100% |
| 10 | Yiling Chen, Yang Liu, and Chara Podimata (2020) Learning strategy-aware linear classifiers | 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 | Incorporating Preferences Into Treatment Assignment Problems | 0.874 | 5 | 2 |
| 2 | Policy Learning with Competing Agents | 0.585 | 3 | 1 |
| 3 | Externally Valid Policy Choice | 0.405 | 1 | 1 |
| 4 | Robust Network Targeting with Multiple Nash Equilibria | 0.405 | 1 | 1 |
| 5 | Leave No One Undermined: Policy Targeting with Regret Aversion | 0.405 | 1 | 1 |