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Frey's Scenario F simulation mentioned in account of the Democratic Party's tribulations

U-M Poverty Solutions funds nine projects

Dynarski says NY's Excelsior Scholarship Program could crowd out low-income and minority students

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Workshops on EndNote, NIH reporting, and publication altmetrics, Jan 26 through Feb 7, ISR

2017 PAA Annual Meeting, April 27-29, Chicago

NIH funding opportunity: Etiology of Health Disparities and Health Advantages among Immigrant Populations (R01 and R21), open Jan 2017

Russell Sage 2017 Summer Institute in Computational Social Science, June 18-July 1. Application deadline Feb 17.

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Mon, Jan 23, 2017 at noon:
Decline of cash assistance and child well-being, Luke Shaefer

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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