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Virtual BSU Seminar: 'Confidence intervals for policy evaluation in adaptive experiments’

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  • UserDr Vitor Hadad, Stanford University
  • ClockThursday 27 May 2021, 14:00-15:00
  • HouseVirtual Seminar .

If you have a question about this talk, please contact Alison Quenault .

If you would like to join this virtual seminar, please email alison.quenault@mrc-bsu.cam.ac.uk for more information.

Randomized controlled trials are central to the scientific process, but they can be costly. For example, a clinical trial may assign patients to treatments that are detrimental to them. Adaptive experimental designs, such as multiarmed bandit algorithms, reduce costs by increasing the probability of assigning promising treatments over the course of the experiment. However, because observations collected by these methods are dependent and their distribution is nonstationary, statistical inference can be challenging. We propose a treatment-effect estimator that has an asymptotically unbiased and normal test statistic under straightforward, relatively weak conditions on the adaptive design. This estimator generalizes for a variety of parameters of interest.

This talk is part of the MRC Biostatistics Unit Seminars series.

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