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Xu et al find lower cognition at midlife for adults born during China's 1959-61 famine

UM's Wolfers on separating deep expertise from partisanship in analyses of economic condtions

Findings by Burgard, Kalousova, and Seefeldt on the mental health impact of job insecurity

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Apply by Jan 8 for NIA-supported PSC post-doc fellowship, to begin Sept 1, 2018

On Giving Blue Day, help support the next generation through the PSC Alumni Grad Student Support Fund or ISR's Next Gen Fund

Bailey et al. find higher income among children whose parents had access to federal family planning programs in the 1960s and 70s

U-M's campus climate survey results discussed in CHE story

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Mon, Jan 22, 2018, noon: Narayan Sastry

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

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