Masahiro Kato, Akihiro Oga, Wataru Komatsubara, Ryo Inokuchi
arXiv 6 Mar 2024 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2403.03589 · PDF · DOI · OpenAlex · Extracted main text
This study designs an adaptive experiment for efficiently estimating average treatment effects (ATEs). In each round of our adaptive experiment, an experimenter sequentially samples an experimental unit, assigns a treatment, and observes the corresponding outcome immediately. At the end of the experiment, the experimenter estimates an ATE using the gathered samples. The objective is to estimate the ATE with a smaller asymptotic variance. Existing studies have designed experiments that adaptively optimize the propensity score (treatment-assignment probability). As a generalization of such an approach, we propose optimizing the covariate density as well as the propensity score. First, we derive the efficient covariate density and propensity score that minimize the semiparametric efficiency bound and find that optimizing both covariate density and propensity score minimizes the semiparametric efficiency bound more effectively than optimizing only the propensity score. Next, we design an adaptive experiment using the efficient covariate density and propensity score sequentially estimated during the experiment. Lastly, we propose an ATE estimator whose asymptotic variance aligns with the minimized semiparametric efficiency bound.
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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, 2008 | 1.000 | 10 | 5 | 100% |
| 2 | Hahn, J., Hirano, K., and Karlan, D (2011) Adaptive experimental design using the propensity score | 1.000 | 8 | 4 | 100% |
| 3 | Kato, M., Ishihara, T., Honda, J., and Narita, Y (2020) Adaptive experimental design for efficient treatment effect estimation: Randomized allocation via contextual bandit algorithm self | 0.969 | 11 | 6 | 91% |
| 4 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 4 | 3 | 100% |
| 5 | Uehara, M., Kato, M., and Yasui, S (2020) Off-policy evaluation and learning for external validity under a covariate shift self | 0.794 | 6 | 4 | 50% |
| 6 | Tabord-Meehan, M (2022) Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials | 0.737 | 3 | 2 | 100% |
| 7 | Dai, J., Gradu, P., and Harshaw, C (2023) CLIP-OGD: An experimental design for adaptive neyman allocation in sequential experiments | 0.644 | 2 | 2 | 100% |
| 8 | Kato, M., McAlinn, K., and Yasui, S (2021) The adaptive doubly robust estimator and a paradox concerning logging policy self | 0.644 | 2 | 2 | 100% |
| 9 | Shimodaira, H (2000) Improving predictive inference under covariate shift by weighting the log-likelihood function | 0.644 | 2 | 2 | 100% |
| 10 | Sugiyama, M (2006) Active learning in approximately linear regression based on conditional expectation of generalization error | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 82 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Efficient Adaptive Experimental Design for Average Treatment Effect Estimation | 0.405 | 1 | 1 |