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

PSC In The News

RSS Feed icon

Thompson casts doubt on the rehabilitative intentions of prison labor

Inglehart says European social democracy is a victim of its own success

Bound, Khanna, and Morales find multiple effects of H1-B visas on US tech industry

More News

Highlights

U-M participants at PAA Annual Meeting, April 27-29

Heather Ann Thompson wins Bancroft Prize for History for 'Blood in the Water'

Michigan ranks in USN&WR top-10 grad schools for sociology, public health, labor economics, social policy, social psychology

Paula Lantz to speak at Women in Health Leadership Summit, March 24, 2:30-5:30 Michigan League

More Highlights

Next Brown Bag

Mon, April 10, 2017, noon:
Elizabeth Bruch

Yu Xie photo

Estimating Heterogeneous Treatment Effects with Observational Data

Publication Abstract

Download PDF versionXie, Yu, Jennie Brand, and Ben Jann. 2011. "Estimating Heterogeneous Treatment Effects with Observational Data." PSC Research Report No. 11-729. 2 2011.

Heterogeneous treatment effects are widely recognized but seldom studied empirically in quantitative sociological research. We suspect that lack of accessible statistical methods is one reason why heterogeneous treatment effects are not routinely assessed and reported. In this paper, we discuss a practical approach to studying heterogeneous treatment effects, under the same assumption commonly underlying regression analysis: ignorability. We specifically describe two methods. For the first method (SM-HTE), we begin by estimating propensity scores for the probability of treatment given a set of observed covariates for each unit and construct balanced propensity score strata; we then estimate propensity score stratum-specific average treatment effects and evaluate a trend across the strata-specific treatment effects. For the second method (MS-HTE), we match control units to treated units based on the propensity score and transform the data into treatment-control comparisons at the most elementary level at which such comparisons can be constructed; we then estimate treatment effects as a function of the propensity score by fitting a non-parametric model as a smoothing device. We illustrate the application of the two methods with a concrete empirical example.

Browse | Search : All Pubs | Next