arXiv 17 Feb 2021 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2102.08975 · PDF · DOI · OpenAlex · Extracted main text
Adaptive experiments, including efficient average treatment effect estimation and multi-armed bandit algorithms, have garnered attention in various applications, such as social experiments, clinical trials, and online advertisement optimization. This paper considers estimating the mean outcome of an action from samples obtained in adaptive experiments. In causal inference, the mean outcome of an action has a crucial role, and the estimation is an essential task, where the average treatment effect estimation and off-policy value estimation are its variants. In adaptive experiments, the probability of choosing an action (logging policy) is allowed to be sequentially updated based on past observations. Due to this logging policy depending on the past observations, the samples are often not independent and identically distributed (i.i.d.), making developing an asymptotically normal estimator difficult. A typical approach for this problem is to assume that the logging policy converges in a time-invariant function. However, this assumption is restrictive in various applications, such as when the logging policy fluctuates or becomes zero at some periods. To mitigate this limitation, we propose another assumption that the average logging policy converges to a time-invariant function and show the doubly robust (DR) estimator's asymptotic normality. Under the assumption, the logging policy itself can fluctuate or be zero for some actions. We also show the empirical properties by simulations.
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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 | van der Laan, M. J (2008) The construction and analysis of adaptive group sequential designs | 1.000 | 10 | 3 | 100% |
| 2 | Luedtke, A. R. and M. J. van der Laan (2016, Apr) (2016) Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy | 1.000 | 7 | 3 | 100% |
| 3 | Hahn, J., K. Hirano, and D. Karlan (2011) Adaptive experimental design using the propensity score | 1.000 | 6 | 3 | 100% |
| 4 | van der Laan, M. J. and S. D. Lendle (2014) Online targeted learning | 0.971 | 12 | 4 | 92% |
| 5 | Kato, M (2020) Theoretical and experimental comparison of off-policy evaluation from dependent samples self | 0.969 | 11 | 4 | 91% |
| 6 | Kato, M., T. Ishihara, J. Honda, and Y. Narita (2020) Adaptive experimental design for efficient treatment effect estimation: Randomized allocation via contextual bandit algorithm self | 0.950 | 7 | 4 | 86% |
| 7 | Hadad, V., D. A. Hirshberg, R. Zhan, S. Wager, and S. Athey (2019) Confidence intervals for policy evaluation in adaptive experiments | 0.928 | 4 | 3 | 100% |
| 8 | Dudḱ, M., J. Langford, and L. Li (2011) Doubly Robust Policy Evaluation and Learning | 0.843 | 3 | 3 | 100% |
| 9 | Zhang, K., L. Janson, and S. Murphy (2020) Inference for batched bandits | 0.811 | 4 | 2 | 100% |
| 10 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 55 scored citations.