Selecting and ranking individualized treatment rules with unmeasured confounding


Optimal individualized treatment rules try to assign the best treatment to every individual, but it may be very sensitive to unmeasured confounding bias for groups of people exhibiting small treatment effect in the observational study. We give a quantification of this idea using Rosenbaum’s sensitivity analysis model and propose some general ways to select and rank individualized treatment rules using multiple testing procedures.

Journal of the American Statistical Association (2020+)
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