Stefan Wager, Kuang Xu
arXiv 6 Mar 2019 · Mathematics — Optimization
arXiv:1903.02124 · PDF · Extracted main text
Classical approaches to experimental design assume that intervening on one unit does not affect other units. There are many important settings, however, where this non-interference assumption does not hold, as when running experiments on supply-side incentives on a ride-sharing platform or subsidies in an energy marketplace. In this paper, we introduce a new approach to experimental design in large-scale stochastic systems with considerable cross-unit interference, under an assumption that the interference is structured enough that it can be captured via mean-field modeling. Our approach enables us to accurately estimate the effect of small changes to system parameters by combining unobstrusive randomization with lightweight modeling, all while remaining in equilibrium. We can then use these estimates to optimize the system by gradient descent. Concretely, we focus on the problem of a platform that seeks to optimize supply-side payments p in a centralized marketplace where different suppliers interact via their effects on the overall supply-demand equilibrium, and show that our approach enables the platform to optimize p in large systems using vanishingly small perturbations.
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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 | Michael G Hudgens and M Elizabeth Halloran (2008) Toward causal inference with interference | 1.000 | 7 | 3 | 100% |
| 2 | Sarah Baird, J Aislinn Bohren, Craig McIntosh, and Berk Özler (2018) Optimal design of experiments in the presence of interference | 1.000 | 5 | 3 | 100% |
| 3 | Sachin Adlakha, Ramesh Johari, and Gabriel Y Weintraub (2015) Equilibria of dynamic games with many players: Existence, approximation, and market structure | 0.843 | 3 | 3 | 100% |
| 4 | Hugo A Hopenhayn (1992) Entry, exit, and firm dynamics in long run equilibrium | 0.843 | 3 | 3 | 100% |
| 5 | Gabriel Y Weintraub, C Lanier Benkard, and Benjamin Van Roy (2008) Markov perfect industry dynamics with many firms | 0.843 | 3 | 3 | 100% |
| 6 | Michael P Leung (2020) Treatment and spillover effects under network interference | 0.811 | 4 | 2 | 100% |
| 7 | Peter M Aronow and Cyrus Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.737 | 3 | 2 | 100% |
| 8 | Susan Athey, Dean Eckles, and Guido W Imbens (2018) Exact p-values for network interference | 0.737 | 3 | 2 | 100% |
| 9 | Guillaum W Basse, Avi Feller, and Panos Toulis (2019) Randomization tests of causal effects under interference | 0.737 | 3 | 2 | 100% |
| 10 | Dean Eckles, Brian Karrer, and Johan Ugander (2017) Design and analysis of experiments in networks: Reducing bias from interference | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 78 scored citations.