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

PSC In The News

RSS Feed icon

Geronimus says black-white differences in mortality "help silence black voices in the electorate"

Do universities need more conservative thinkers?

Starr critical of risk assessment scores for sentencing

Highlights

Presentation on multilevel modeling using Stata, July 26th, noon, 6050 ISR

Frey's new report explores how the changing US electorate could shape the next 5 presidential elections, 2016 to 2032

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

Elizabeth Bruch promoted to Associate Professor

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