Speaker
Description
As medicine enters an era of precision, the challenge for statistics is no longer whether personalized care is possible, but how best to translate its potential into clinical practice. Zhao et al. (2012) formulated the personalized medicine problem as finding the optimal individual treatment rule (ITR) by maximizing the expected clinical responses. More recently, Lei and Candès (2021) developed interval estimates for individual treatment effects using conformal inference in observational studies. There is also a line of research on Digital Health using data from wearable devices.
Different from the above lines of research, this presentation contributes to personalized medicine by addressing uncertainty quantification in randomized controlled trials (RCTs). While point estimation of counterfactual efficacy has been understood by Jerzy Neyman since 1923, uncertainty quantification in this setting has remained unresolved. We introduce Counterfactual Uncertainty Quantification (CUQ), enabled by a new statistical modeling principle called ETZ, which often yields lower variability than traditional UQ methods in personalized medicine. We also highlight the risks of using predictors measured with error and discuss conditions under which counterfactual estimates remain unbiased. Finally, we emphasize the need for caution when estimating subgroup effects, as bias can arise in both Real Human approach and the Digital Twin AI technique.
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