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Thompson says America must "unchoose" policies that have led to mass incarceration

Axinn says new data on campus rape will "allow students to see for themselves the full extent of this problem"

Frey says white population is growing in Detroit and other large cities


Susan Murphy to speak at U-M kickoff for data science initiative, Oct 6, Rackham

Andrew Goodman-Bacon, former trainee, wins 2015 Nevins Prize for best dissertation in economic history

Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

Next Brown Bag

Monday, Oct 5 at noon, 6050 ISR
Colter Mitchell: Biological consequences of poverty

Graph of treatment effects

Analyzing response variability to common treatment

1/27/2014 feature story

Yu Xie, Jeffrey Smith, and Daniel Almirall develop statistical methods to estimate heterogeneous treatment effects and a set of tools to help analyze these effects in demographic research.

More Information.

Yu Xie
Jeffrey A. Smith
Daniel Almirall

Project Information:

Heterogeneous Treatment Effects in Demographic Research

One essential feature common to all demographic phenomena is variability across units of analysis. Individuals differ greatly not only in attributes and outcomes of interest to social and behavioral scientists, but also in how they respond to a common treatment, intervention, or stimulation. We call the second type of variability 'heterogeneous treatment effects.' The proposed research assembles an interdisciplinary team from sociology, economics, statistics, and demography to investigate the consequences of and methodological approaches to heterogeneous treatment effects. The proposed research has five specific aims: 1. It will demonstrate, with combined observational and experimental data, heterogeneous treatment effects in demographic research. 2. It will develop statistical methods for estimating heterogeneous treatment effects using the instrumental variable approach with quasi-experimental data. 3. It will further demonstrate the usefulness of estimating heterogeneous treatment effects in observational data with propensity score methods, especially with applications to studies of the impact of family-level shocks on children?s psychosocial skills. 4. It will demonstrate, through micro-level simulations, that in the presence of heterogeneous treatment effects, the use of standard statistical methods may give rise to treatment effect estimates with compositional biases. 5. It will develop a set of diagnostic and analytical tools that will help researchers and practitioners to analyze heterogeneous treatment effects in demographic research.

Yu Xie, Jeffrey A. Smith, Hongwei Xu

Feature Archive.