Yu Xie photo

Estimating Heterogeneous Treatment Effects with Observational Data

Publication Abstract

PDF Xie, 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 | Next

PSC In The News

RSS Feed icon

Mehta makes it clear why young people are leading the rise of COVID cases in Michigan: Socializing

More News

Highlights

Frey's Social Science Data Analysis Network, SSDAN wins 2020 MERLOT Sociology Classics Award

Doing COVID-19 research? These data tools can help!

More Highlights


Connect with PSC follow PSC on Twitter Like PSC on Facebook