arXiv 1 Sep 2022 · Statistics — Methodology · 11 citations (OpenAlex)
arXiv:2209.00197 · PDF · DOI · OpenAlex · Extracted main text
The switchback is an experimental design that measures treatment effects by repeatedly turning an intervention on and off for a whole system. Switchback experiments are a robust way to overcome cross-unit spillover effects; however, they are vulnerable to bias from temporal carryovers. In this paper, we consider properties of switchback experiments in Markovian systems that mix at a geometric rate. We find that, in this setting, standard switchback designs suffer considerably from carryover bias: Their estimation error decays as $T^{-1/3}$ in terms of the experiment horizon $T$, whereas in the absence of carryovers a faster rate of $T^{-1/2}$ would have been possible. We also show, however, that judicious use of burn-in periods can considerably improve the situation, and enables errors that decay as $\log(T)^{1/2}T^{-1/2}$. Our formal results are mirrored in an empirical evaluation.
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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 | Bojinov, Iavor and Simchi-Levi, David and Zhao, Jinglong (2023) Design and analysis of switchback experiments | 0.923 | 14 | 4 | 79% |
| 2 | Glynn, Peter W and Johari, Ramesh and Rasouli, Mohammad (2020) Adaptive experimental design with temporal interference: A maximum likelihood approach | 0.874 | 6 | 2 | 100% |
| 3 | Farias, Vivek and Li, Andrew and Peng, Tianyi and Zheng, Andrew (2022) Markovian interference in experiments | 0.843 | 4 | 3 | 75% |
| 4 | Imbens, Guido W and Rubin, Donald B (2015) Causal Inference in Statistics, Social, and Biomedical Sciences | 0.843 | 5 | 5 | 60% |
| 5 | Xiong, Ruoxuan and Chin, Alex and Taylor, Sean (2023) Bias-variance tradeoffs for designing simultaneous temporal experiments | 0.843 | 3 | 3 | 100% |
| 6 | Künsch, Hans R (1989) The jackknife and the bootstrap for general stationary observations | 0.737 | 3 | 3 | 67% |
| 7 | Neyman, Jersey (1923) Sur les applications de la théorie des probabilités aux experiences agricoles: Essai des principes | 0.737 | 3 | 3 | 67% |
| 8 | Aronow, Peter M and Samii, Cyrus (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.737 | 3 | 2 | 100% |
| 9 | Bojinov, Iavor and Shephard, Neil (2019) Time series experiments and causal estimands: exact randomization tests and trading | 0.737 | 3 | 2 | 100% |
| 10 | Leung, Michael P (2022) Rate-optimal cluster-randomized designs for spatial interference | 0.737 | 3 | 2 | 100% |
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