Aaditya Ramdas develops statistical inference methods that remain valid when data are examined sequentially. His research also includes multiple testing and machine learning. A central practical question is how to report evidence while allowing data collection to continue or stop in response to the observations.[ref]
He coauthored distillation for the conditional randomization test, combining conditional independence testing with flexible test statistics and reduced computational cost. The calibration relies on the assumed feature conditional distribution.[ref]
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