title: 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.
authors: University of Michigan.
authors: Yu
authors: Y
authors: Xie
references: Am Stat. 1985 Aug;39(3):176-85
references: 12267300
references: J Natl Cancer Inst. 1959 Jan;22(1):173-203
references: 13621204
references: Am Econ Rev. 2011 Oct;101(6):2754-2781
references: 25110355
references: Soc Sci Res. 2011 May;40(3):796-810
references: 21625367
references: Ann Intern Med. 1997 Oct 15;127(8 Pt 2):757-63
references: 9382394
references: Sociol Methodol. 2012 Aug;42(1):314-347
references: 23482633
references: Proc Am Philos Soc. 2001 Dec;145(4):488-95
references: 11894859