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

PSC In The News

RSS Feed icon

Kruger says reports of phantom mobile phone ringing/vibrating more common among anxious

Stafford says too early to say whether stock market declines will curtail Americans' spending

Eisenberg says many colleges now train campus personnel to spot and refer troubled college students

Highlights

Call for papers: Conference on Integrating Genetics and the Social Sciences, Oct 21-22, 2016, CU-Boulder

PRB training program in policy communication for pre-docs. Application deadline, 2.28.2016

Call for proposals: PSID small grants for research on life course impacts on later life wellbeing

PSC News, fall 2015 now available

Next Brown Bag

Monday, Feb 1 at noon, 6050 ISR-Thompson
Sarah Miller

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. February 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