Causal inference and heterogeneity bias in social science

Abstract

Because of population heterogeneity, causal inference with observational data in social science may suffer from two possible sources of bias: (1) bias in unobserved pretreatment factors affecting the outcome even without treatment; and (2)bias due to heterogeneity in treatment effects. Even when we control for observed covariates, these two biases may occur if the classic ignorability assumption is untrue. In cases where the ignorability assumption is true, “composition bias” can occur if treatment propensity is systematically associated with heterogeneous treatment effects.