Keegan Harris, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu
arXiv 25 Nov 2022 · Econometrics · publishedACM SIGMETRICS Performance Evaluation Review (2024) · 1 citations (OpenAlex)
arXiv:2211.14236 · PDF · DOI · OpenAlex · Extracted main text
We consider the problem of decision-making using panel data, in which a decision-maker gets noisy, repeated measurements of multiple units (or agents). We consider a setup where there is a pre-intervention period, when the principal observes the outcomes of each unit, after which the principal uses these observations to assign a treatment to each unit. Unlike this classical setting, we permit the units generating the panel data to be strategic, i.e. units may modify their pre-intervention outcomes in order to receive a more desirable intervention. The principal's goal is to design a strategyproof intervention policy, i.e. a policy that assigns units to their utility-maximizing interventions despite their potential strategizing. We first identify a necessary and sufficient condition under which a strategyproof intervention policy exists, and provide a strategyproof mechanism with a simple closed form when one does exist. Along the way, we prove impossibility results for strategic multiclass classification, which may be of independent interest. When there are two interventions, we establish that there always exists a strategyproof mechanism, and provide an algorithm for learning such a mechanism. For three or more interventions, we provide an algorithm for learning a strategyproof mechanism if there exists a sufficiently large gap in the principal's rewards between different interventions. Finally, we empirically evaluate our model using real-world panel data collected from product sales over 18 months. We find that our methods compare favorably to baselines which do not take strategic interactions into consideration, even in the presence of model misspecification.
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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 | Yiling Chen, Yang Liu, and Chara Podimata (2020) Learning strategy-aware linear classifiers | 0.928 | 4 | 3 | 100% |
| 2 | Anish Agarwal, Devavrat Shah, and Dennis Shen (2020) Synthetic interventions self | 0.811 | 4 | 2 | 100% |
| 3 | Anish Agarwal, Keegan Harris, Justin Whitehouse, and Zhiwei Steven Wu (2023) Adaptive Principal Component Regression with Applications to Panel Data self | 0.811 | 4 | 2 | 100% |
| 4 | Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, and Zhiwei… (2018) Strategic classification from revealed preferences. In Proceedings of the 2018 ACM Conference on Economics and Computation. 55–70 | 0.737 | 3 | 2 | 100% |
| 5 | Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Woott… (2016) Strategic classification. In Proceedings of the 2016 ACM conference on innovations in theoretical computer science. 111–122 | 0.737 | 3 | 2 | 100% |
| 6 | Keegan Harris, Hoda Heidari, and Steven Z Wu (2021) Stateful strategic regression self | 0.737 | 3 | 2 | 100% |
| 7 | Jon Kleinberg and Manish Raghavan (2020) How do classifiers induce agents to invest effort strategically? | 0.644 | 2 | 2 | 100% |
| 8 | Yahav Bechavod, Chara Podimata, Steven Wu, and Juba Ziani (2022) Information discrepancy in strategic learning. In International Conference on Machine Learning. PMLR, 1691–1715 self | 0.585 | 3 | 1 | 100% |
| 9 | Alberto Abadie and Javier Gardeazabal (2003) The economic costs of conflict: A case study of the Basque Country | 0.511 | 2 | 1 | 100% |
| 10 | Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 55 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration | 0.737 | 3 | 2 |
| 2 | Incorporating Preferences Into Treatment Assignment Problems | 0.585 | 3 | 1 |
| 3 | Adaptive Principal Component Regression with Applications to Panel Data | 0.511 | 2 | 2 |