arXiv 10 Apr 2025 · Statistics — Machine Learning
arXiv:2504.08836 · PDF · DOI · OpenAlex · Extracted main text
Researchers and practitioners often wish to measure treatment effects in settings where units interact via markets and recommendation systems. In these settings, units are affected by certain shared states, like prices, algorithmic recommendations or social signals. We formalize this structure, calling it shared-state interference, and argue that our formulation captures many relevant applied settings. Our key modeling assumption is that individuals' potential outcomes are independent conditional on the shared state. We then prove an extension of a double machine learning (DML) theorem providing conditions for achieving efficient inference under shared-state interference. We also instantiate our general theorem in several models of interest where it is possible to efficiently estimate the average direct effect (ADE) or global average treatment effect (GATE).
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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 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W.… (2018) Double/debiased machine learning for treatment and structural parameters | 0.944 | 19 | 5 | 84% |
| 2 | I. Bojinov, D. Simchi-Levi, and J. Zhao (2009) Design and Analysis of Switchback Experiments, Apr. 2022 | 0.874 | 6 | 2 | 100% |
| 3 | D. Ballinari and A. Wehrli (2024) Semiparametric inference for impulse response functions using double/debiased machine learning, Nov. 2024 | 0.843 | 4 | 4 | 75% |
| 4 | V. F. Farias, H. Li, T. Peng, X. Ren, H. Zhang, and A. Zheng (2023) Correcting for Interference in Experiments: A Case Study at Douyin, May 2023 | 0.811 | 4 | 2 | 100% |
| 5 | R. Johari, H. Li, I. Liskovich, and G. Weintraub (2021) Experimental Design in Two-Sided Platforms: An Analysis of Bias, 2021 | 0.811 | 4 | 2 | 100% |
| 6 | S. Li, R. Johari, X. Kuang, and S. Wager (2024) Experimenting under Stochastic Congestion, Oct. 2024 | 0.811 | 4 | 2 | 100% |
| 7 | E. Munro (2024) Causal Inference under Interference through Designed Markets | 0.737 | 4 | 2 | 75% |
| 8 | S. Wager (2024) Causal Inference: A Statistical Learning Approach | 0.737 | 3 | 2 | 100% |
| 9 | V. F. Farias, A. A. Li, T. Peng, and A. Zheng (2022) Markovian Interference in Experiments, June 2022 | 0.644 | 4 | 1 | 100% |
| 10 | S. Meyn and R. L. Tweedie (2009) Markov Chains and Stochastic Stability | 0.644 | 3 | 2 | 67% |
Showing the top 10 of 49 scored citations.