How to estimate causal effects associated with family planning? An introduction to Prince BART, a new approach to effect estimation based on principal stratification and Bayesian non-parametric models

Abstract

There is interest in learning about the causal effect of family planning (FP) on empowerment-related outcomes. Experimental data related to this question are available from trials in which FP programs increase access to FP. However, estimating the effect of interest is challenging because FP and empowerment-related variables are likely to be confounded. We propose a new approach, Prince BART, to estimate the causal effect of FP on other outcomes of interest, among women affected by a FP program. Prince BART combines principal stratification, to account for differences in FP behavior based on exposure to a FP program, with Bayesian Additive Regression Trees (BART), to non-parametrically model stratum membership and relationships between variables. Prince Bart allows for estimation of effect heterogeneity. We use Prince BART to assess the causal effect of modern contraceptive use on employment in six cities in Nigeria, based on quasi-experimental data from a FP program trial during the first half of the 2010s. In the sample, we find a positive overall effect of contraceptive use on employment and substantial differences in effect sizes across subgroups defined by baseline covariates such as marital status, work at baseline, and wealth or education. The modeling approach and results can be used to inform future analyses seeking to understand the causal relationship between family planning use and other outcomes, e.g., related to empowerment.