Causal inference and heterogeneity bias in social science

Issue title: Complex Socio-Technical Systems – Understanding and Influencing Causality of Change

Article type: Research Article

Authors: Xie, Yu

Note: [] Corresponding author. E-mail: yuxie@umich.edu

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.

DOI: 10.3233/IKS-2012-0197

Journal: Information Knowledge Systems Management, vol. 10, no. 1-4, pp. 279-289, 2011