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Researchers have long theorized about the processes through which childhood experiences shape life outcomes. However, statistical models in the social science often have poor predictive performance. Despite this track record, policy makers are increasingly considering using complex predictive models for high-stakes decisions in settings such as criminal justice and child protective services.
In this talk, we present results from the Fragile Families Challenge, a scientific mass collaboration designed to assess the limits of predictability of life outcomes and improve our understanding of these limits. Using data from the Fragile Fragile Families and Child Wellbeing Study, a high-quality, birth cohort study that has followed about 5,000 mainly disadvantaged families for the past 15 years, 457 researchers built predictive models of six life outcomes, such as a child’s grades in school or whether the family would be evicted from their home. Research participants in the Challenge could use any theoretical, statistical, or machine learning approach they wished and could draw on the more than 12,000 features that had been measured about the child, parents, and family since the birth of the child. All predictions were evaluated on held-out data. Our empirical results have implications for social science theory, data, and methods and for algorithmic decision-making in high-stakes social settings.
Matthew, a professor of sociology who has pioneered uses of data and digital technologies in social research, was appointed interim director of Princeton University’s Center for Information Technology Policy on July 1, 2019.
Matthew is affiliated with several other Princeton’s interdisciplinary research centers, including: the Office for Population Research, the Center for Health and Wellbeing, and the Center for Statistics and Machine Learning. His research interests include social networks and computational social science. He is the author of Bit by Bit: Social Research in the Digital Age.
Matthew’s research has been published in journals such as Science, PNAS, Sociological Methodology, and Journal of the American Statistical Association. His papers have won the Outstanding Article Award from the Mathematical Sociology Section of the American Sociological Association and the Outstanding Statistical Application Award from the American Statistical Association. Popular accounts of his work have appeared in the New York Times, Wall Street Journal, Economist, and New Yorker. Matthew is currently on the Board of Directors of Mathematica Policy Research. Matthew’s research has been funded by the National Science Foundation, National Institutes of Health, Joint United Nations Program for HIV/AIDS (UNAIDS), Russell Sage Foundation, Alfred P. Sloan Foundation, Facebook, and Google. During sabbaticals from Princeton, he has been a Visiting Professor at Cornell Tech and a Senior Research at Microsoft Research. During the 2018-19 academic year, he was a professor in residence at the New York Times.
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