Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Lam discusses shifts in global population past and future

Thompson says LGBT social movement will bring new strength in push for tighter gun control

Yang says devalued pound will decrease resources for the families of migrant workers in Britain


Overview of Michigan's advanced research computing resources, Monday, June 27, 9-10:30 am, BSRB - Kahn Auditorium

U-M's Data Science Initiative offers expanded consulting services via CSCAR

Elizabeth Bruch promoted to Associate Professor

Susan Murphy elected to the National Academy of Sciences

Next Brown Bag

PSC Brown Bags
will resume fall 2016

Yu Xie photo

Population Heterogeneity and Causal Inference

Publication Abstract

Download PDF versionXie, Yu. 2011. "Population Heterogeneity and Causal Inference." PSC Research Report No. 11-731. March 2011.

Population heterogeneity is ubiquitous in social science research. The very objective of social science is not to discover abstract and universal laws but to understand population heterogeneity. Due to population heterogeneity, causal inference with observational data in social science is impossible without strong assumptions. There are two potential sources of bias. The first is bias in unobserved pretreatment factors affecting the outcome even in the absence of treatment. The second is bias due to heterogeneity in treatment effects. In this paper, I show how “composition bias” due to population heterogeneity arises when treatment propensity is systematically associated with heterogeneous treatment effects. Of particular interest is the way in which composition bias, a form of selection bias, arises even under the classic assumption of ignorability, as I demonstrate with a simple simulation example.

Browse | Search : All Pubs | Next