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Machine Learning in Montpellier, Theory & Practice – Emma Torrini (Universita degli studi Firenze)
The presence of intermediate variables that occur after treatment assignment and potentially influence the outcome complicates the estimation of causal effects, even in randomized trials. Principal stratification is a methodology employed to estimate the causal effect of a treatment on an outcome in the presence of such intermediate variables. This approach involves the cross-classification of units into latent groups, referred to as principal strata, which are defined by the potential values of the intermediate variable under treatment and control conditions. The focus is on estimating principal causal effects, which are causal effects within these principal strata. The identification of principal causal effects can be achieved through various methods, one of which is the principal score method. At present, principal score methods are available for a binary treatment, binary intermediate variable, and binary/continuous/survival outcome. Motivated by a cardiovascular randomized trial, we take an initial step towards integrating a survival intermediate variable into principal score methods.