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Do universities need more conservative thinkers?

Geronimus says black-white differences in mortality "help silence black voices in the electorate"

Starr critical of risk assessment scores for sentencing

Highlights

Presentation on multilevel modeling using Stata, July 26th, noon, 6050 ISR

Frey's new report explores how the changing US electorate could shape the next 5 presidential elections, 2016 to 2032

U-M's Data Science Initiative offers expanded consulting services via CSCAR

Elizabeth Bruch promoted to Associate Professor

Next Brown Bag

PSC Brown Bags
will resume fall 2016

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.