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Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference.
On the bias, risk and consistency of sample means in multi-armed bandits
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Online debiasing for adaptively collected high-dimensional data
Deshpande, Y., Javanmard, A., and Mehrabi, M. (2019) · 1911
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On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
Thompson, W. R. (1933) · 1933
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Some aspects of the sequential design of experiments
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Central limit theorems for martingales with discrete or continuous time
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Selection paradoxes of bayesian inference
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Estimation of regression coefficients when some regressors are not always observed
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Estimation after adaptive allocation
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On conditional versus marginal bias in multi-armed bandits
Shin, J., Ramdas, A., and Rinaldo, A. (2020) · 2002
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Zhang, K. W., Janson, L., and Murphy, S. A. (2020) · 2002
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The nonstochastic multiarmed bandit problem
Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E. (2003) · 2003
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The sample complexity of exploration in the multi-armed bandit problem
Mannor, S. and Tsitsiklis, J. N. (2004) · 2004
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The theory of response-adaptive randomization in clinical trials
Hu, F. and Rosenberger, W. F. (2006) · 2006
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Self-Normalized Processes: Limit theory and Statistical Applications
de la Peña, V. H., Lai, T. L., and Shao, Q.-M. (2008) · 2008
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The construction and analysis of adaptive group sequential designs
van der Laan, M. J. (2008) · 2008
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Pure exploration in finitely-armed and continuous-armed bandits
Bubeck, S., Munos, R., and Stoltz, G. (2011) · 2011
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Unbiased estimation for response adaptive clinical trials
Bowden, J. and Trippa, L. (2017) · 2017
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Accurate inference for adaptive linear models
Deshpande, Y., Mackey, L., Syrgkanis, V., and Taddy, M. (2017) · 2017
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Estimation considerations in contextual bandits
Dimakopoulou, M., Zhou, Z., Athey, S., and Imbens, G. (2017) · 2017
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Augmented minimax linear estimation
Hirshberg, D. A. and Wager, S. (2017) · 2017
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Parametric-rate inference for one-sided differentiable parameters
Luedtke, A. R. and van der Laan, M. J. (2018) · 2018
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Estimation bias in multi-armed bandit algorithms for search advertising
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Optimal inference after model selection
Fithian, W., Sun, D., and Taylor, J. (2014) · 2014
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Online targeted learning
van der Laan, M. J. and Lendle, S. D. (2014) · 2014
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Causal inference in statistics, social, and biomedical sciences
Imbens, G. W. and Rubin, D. B. (2015) · 2015
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Randomization in clinical trials: theory and practice
Rosenberger, W. F. and Lachin, J. M. (2015) · 2015
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Locally robust semiparametric estimation
Chernozhukov, V., Escanciano, J. C., Ichimura, H., Newey, W. K., and Robins, J. M. (2016) · 2016
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Why adaptively collected data have negative bias and how to correct for it
Nie, X., Tian, X., Taylor, J., and Zou, J. (2018) · 2018
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A tutorial on thompson sampling
Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., and Wen, Z. (2018) · 2018
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Inference on winners
Andrews, I., Kitagawa, T., and McCloskey, A. (2019) · 2019
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Time-uniform chernoff bounds via nonnegative supermartingales
Howard, S. R., Ramdas, A., McAuliffe, J., and Sekhon, J. (2020) · 2020
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Adaptive treatment assignment in experiments for policy choice
Kasy, M. and Sautmann, A. (2020) · 2020
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Bandit Algorithms
Lattimore, T. and Szepesvári, C. (2020) · 2020
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Simple bayesian algorithms for best-arm identification
Russo, D. (2020) · 2020
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Time-uniform, nonparametric, non-asymptotic confidence sequences
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