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Statistical Models and Causal Inference
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  • Cited by 11
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Lu, Bo Cai, Dingjiao and Tong, Xingwei 2018. Testing causal effects in observational survival data using propensity score matching design. Statistics in Medicine, Vol. 37, Issue. 11, p. 1846.

    Caughey, Devin Dafoe, Allan and Seawright, Jason 2017. Nonparametric Combination (NPC): A Framework for Testing Elaborate Theories. The Journal of Politics, Vol. 79, Issue. 2, p. 688.

    GOW, IAN D. LARCKER, DAVID F. and REISS, PETER C. 2016. Causal Inference in Accounting Research. Journal of Accounting Research, Vol. 54, Issue. 2, p. 477.

    Kim, Hongsoo Hung, William W. Paik, Myunghee Cho Ross, Joseph S. Zhao, Zhonglin Kim, Gi-Soo and Boockvar, Kenneth 2015. Predictors and outcomes of unplanned readmission to a different hospital. International Journal for Quality in Health Care, Vol. 27, Issue. 6, p. 513.

    Yom, Sean 2015. From Methodology to Practice. Comparative Political Studies, Vol. 48, Issue. 5, p. 616.

    Munenzon, Mikhail 2014. Evaluation of Systematic Trading Programs. The Journal of Trading, Vol. 10, Issue. 1, p. 37.

    Munenzon, Mikhail 2014. Evaluation of Systematic Trading Programs. The Journal of Trading, p. 141212222051008.

    Holm, Anders and Arendt, Jacob Nielsen 2013. Evaluating the performance of simple estimators for probit models with two dummy endogenous regressors. Journal of Statistical Computation and Simulation, Vol. 83, Issue. 6, p. 1156.

    Foster, E. Michael 2013. Reassessing findings from the Fast Track study: problems of method and analysis. Journal of Experimental Criminology, Vol. 9, Issue. 1, p. 109.

    Glazier, Rebecca A. 2013. Divine Direction: How Providential Religious Beliefs Shape Foreign Policy Attitudes. Foreign Policy Analysis, Vol. 9, Issue. 2, p. 127.

    2010. Publications Received. Contemporary Sociology: A Journal of Reviews, Vol. 39, Issue. 3, p. 369.


Book description

David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a 'shoe leather' methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with scepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor 'low-tech' approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman's views.

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