Assessing effect heterogeneity of a randomized treatment using conditional inference trees
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Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel, intuitive approach that combines prognostic score matching and conditional inference trees to characterize effect heterogeneity of a randomized treatment. One key feature that distinguishes our method from alternative approaches is that it controls the Type I error rate, i.e., the probability of identifying effect heterogeneity if none exists. This feature makes our technique particularly appealing in the context of clinical trials, where there may be significant costs associated with erroneously declaring that effects differ across population subgroups.
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