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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 ifdoi:10.3233/iks-2012-0197 pmid:23970824 pmcid:PMC3747843 fatcat:fahsw663premthzbrgfivqbl4q