Weighted Causal Forests with Complex Survey Data for Heterogenous Treatment Effect Estimation

Thomas M. Menino Convention & Exhibition Center 

Causal forests (CFs) have been recently developed to estimate heterogeneous treatment effects using observational data. However, their application in survey studies, particularly population-based complex surveys with designs, has not been evaluated. To address this gap, we develop a weighted CF (wCF) framework by embedding a composite weight that incorporates propensity score (PS) and accounts for survey designs. We conduct extensive simulations to compare wCF with two other methods: an unweighted CF that ignores survey design features; and a naïve weighted CF that incorporates sampling weights but does not account for the other design features. We consider a range of scenarios by varying the degrees of model misspecification, intra-class correlation among observations, and PS overlap. Method performance is evaluated using the average out-of-sample mean squared error and coverage probability. Using data from the Medicare Current Beneficiary Survey (2018–2022), we further illustrate the application of wCF by examining the impact of financial hardship on hospice enrollment among US older adults (≥65 years) with serious illness.

Causal forest

Complex survey data

Machine Learning

Propensity Score

Medicare Current Beneficiary Survey 

Survey Research Methods Section