Masahiro Kato, Takuya Ishihara, Junya Honda, Yusuke Narita
arXiv 13 Feb 2020 · Statistics — Machine Learning · 5 citations (OpenAlex)
arXiv:2002.05308 · PDF · DOI · OpenAlex · Extracted main text
We study how to efficiently estimate average treatment effects (ATEs) using adaptive experiments. In adaptive experiments, experimenters sequentially assign treatments to experimental units while updating treatment assignment probabilities based on past data. We start by defining the efficient treatment-assignment probability, which minimizes the semiparametric efficiency bound for ATE estimation. Our proposed experimental design estimates and uses the efficient treatment-assignment probability to assign treatments. At the end of the proposed design, the experimenter estimates the ATE using a newly proposed Adaptive Augmented Inverse Probability Weighting (A2IPW) estimator. We show that the asymptotic variance of the A2IPW estimator using data from the proposed design achieves the minimized semiparametric efficiency bound. We also analyze the estimator's finite-sample properties and develop nonparametric and nonasymptotic confidence intervals that are valid at any round of the proposed design. These anytime valid confidence intervals allow us to conduct rate-optimal sequential hypothesis testing, allowing for early stopping and reducing necessary sample size.
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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 | Hahn, J., Hirano, K., and Karlan, D (2011) Adaptive experimental design using the propensity score | 1.000 | 9 | 3 | 100% |
| 2 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 6 | 3 | 100% |
| 3 | Balsubramani, A. and Ramdas, A (2016) Sequential Nonparametric Testing with the Law of the Iterated Logarithm, in | 0.971 | 12 | 4 | 92% |
| 4 | Kato, M., McAlinn, K., and Yasui, S (2021) The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy, in self | 0.971 | 12 | 4 | 92% |
| 5 | Howard, S. R., Ramdas, A., McAuliffe, J. D., and Sekhon, J. S (2021) Time-uniform, nonparametric, nonasymptotic confidence sequences | 0.928 | 4 | 3 | 100% |
| 6 | Klaassen, C. A. J (1987) Consistent Estimation of the Influence Function of Locally Asymptotically Linear Estimators | 0.843 | 3 | 3 | 100% |
| 7 | Jamieson, K., Malloy, M., Nowak, R., and Bubeck, S (2014) lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits, in | 0.811 | 4 | 2 | 100% |
| 8 | Waudby-Smith, I., Wu, L., Ramdas, A., Karampatziakis, N., and Mineir… (2024) a), Anytime-valid off-policy inference for contextual bandits | 0.737 | 3 | 2 | 100% |
| 9 | Cook, T., Mishler, A., and Ramdas, A (2024) Semiparametric Efficient Inference in Adaptive Experiments, in | 0.737 | 3 | 2 | 100% |
| 10 | Tabord-Meehan, M (2022) Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials | 0.693 | 9 | 4 | 33% |
Showing the top 10 of 85 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Fixed-Horizon Self-Normalized Inference for Adaptive Experiments via Martingale AIPW/DML with Logged Propensities | 0.644 | 4 | 1 |
| 2 | Fair Adaptive Experiments | 0.511 | 2 | 1 |
| 3 | Double machine learning and design in batch adaptive experiments | 0.405 | 1 | 1 |
| 4 | Benefits and Costs of Adaptive Sampling | 0.405 | 1 | 1 |