sherlock: Causal Machine Learning for Segment Discovery and Analysis

The sherlock R package implements an approach for population segmentation analysis (or subgroup discovery) using recently developed techniques from causal machine learning. Using data from randomized A/B experiments or observational studies (quasi-experiments), sherlock takes as input a set of user-selected candidate segment dimensions – often, a subset of measured pre-treatment covariates – to discover particular segments of the study population based on the estimated heterogeneity of their response to the treatment under consideration. In order to quantify this treatment response heterogeneity, the conditional average treatment effect (CATE) is estimated using a nonparametric, doubly robust framework (Vanderweele et al. 2019; van der Laan and Luedtke 2015; Luedtke and van der Laan 2016b, 2016a), incorporating state-of-the-art ensemble machine learning (van der Laan, Polley, and Hubbard 2007; Coyle et al. 2021) in the estimation procedure.