arXiv 4 Apr 2022 · Statistics — Machine Learning · publishedOperations Research (2025) · 1 citations (OpenAlex)
arXiv:2204.01884 · PDF · DOI · OpenAlex · Extracted main text
Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to such policies, competition arises, complicating estimation of the optimal policy. In this paper, we study capacity-constrained treatment assignment in the presence of such interference. We consider a dynamic model where the decision maker allocates treatments at each time step and heterogeneous agents myopically best respond to the previous treatment assignment policy. When the number of agents is large but finite, we show that the threshold for receiving treatment under a given policy converges to the policy's mean-field equilibrium threshold. Based on this result, we develop a consistent estimator for the policy gradient. In a semi-synthetic experiment with data from the National Education Longitudinal Study of 1988, we demonstrate that this estimator can be used for learning capacity-constrained policies in the presence of 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 | Debopam Bhattacharya and Pascaline Dupas (2012) Inferring welfare maximizing treatment assignment under budget constraints | 1.000 | 8 | 3 | 100% |
| 2 | Stefan Wager and Kuang Xu (2021) Experimenting in equilibrium self | 0.874 | 8 | 2 | 100% |
| 3 | Alex Frankel and Navin Kartik (2019) Improving information from manipulable data | 0.874 | 5 | 2 | 100% |
| 4 | Evan Munro, Stefan Wager, and Kuang Xu (2021) Treatment effects in market equilibrium self | 0.874 | 5 | 2 | 100% |
| 5 | Alex Frankel and Navin Kartik Muddled information | 0.843 | 4 | 3 | 75% |
| 6 | Meena Jagadeesan, Celestine Mendler-Dünner, and Moritz Hardt (2021) Alternative microfoundations for strategic classification | 0.811 | 4 | 2 | 100% |
| 7 | Lydia T Liu, Nikhil Garg, and Christian Borgs (2022) Strategic ranking | 0.811 | 4 | 2 | 100% |
| 8 | Steven J Ingels (1994) National Education Longitudinal Study of 1988: Second follow-up: Student component data file user's manual | 0.644 | 2 | 2 | 100% |
| 9 | Daron Acemoglu and Martin Kaae Jensen (2015) Robust comparative statics in large dynamic economies | 0.585 | 3 | 1 | 100% |
| 10 | Daniel Björkegren, Joshua E Blumenstock, and Samsun Knight (2020) Manipulation-proof machine learning | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 50 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 | 0.585 | 3 | 1 |
| 2 | Statistical Inference for Fisher Market Equilibrium | 0.000 | 2 | 1 |
| 3 | Bootstrapping Fisher Market Equilibrium and First-Price Pacing Equilibrium | 0.000 | 1 | 1 |