Causal Falling Rule Lists
This method addresses the problem of identifying and quantifying treatment effect heterogeneity in causal inference, particularly for domain-specific applications like wage inequality analysis, but it is incremental as it builds on existing rule-based and Bayesian techniques.
The paper introduces Causal Falling Rule Lists (CFRL) to model heterogeneous treatment effects with monotonic decreasing effects across subgroups, applying it to a census wage dataset to identify subgroups with differing wage inequalities between men and women.
A causal falling rule list (CFRL) is a sequence of if-then rules that specifies heterogeneous treatment effects, where (i) the order of rules determines the treatment effect subgroup a subject belongs to, and (ii) the treatment effect decreases monotonically down the list. A given CFRL parameterizes a hierarchical bayesian regression model in which the treatment effects are incorporated as parameters, and assumed constant within model-specific subgroups. We formulate the search for the CFRL best supported by the data as a Bayesian model selection problem, where we perform a search over the space of CFRL models, and approximate the evidence for a given CFRL model using standard variational techniques. We apply CFRL to a census wage dataset to identify subgroups of differing wage inequalities between men and women.