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

PSC In The News

RSS Feed icon

Prescott says sex offender registries may increase recidivism by making offender re-assimilation impossible

Frey says rising numbers of younger minority voters mean Republicans must focus on fiscal not social issues

Work by Garces and Mickey-Pabello cited in NYT piece on lack of black physicians

Highlights

Elizabeth Bruch wins Robert Merton Prize for paper in analytic sociology

Elizabeth Bruch wins ASA award for paper in mathematical sociology

Spring 2015 PSC newletter available now

Formal demography workshop and conference at UC Berkeley, August 17-21

Next Brown Bag

PSC Brown Bags will be back fall 2015


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