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What Now? Some Brief Reflections on Model-Free Data Analysis

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  • Richard Berk

    (University of Pennsylvania)

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    Abstract

    David Freedman’s critique of causal modeling in the social and biomedical sciences was fundamental. In his view, the enterprise was misguided, and there was no technical fix. Far too often, there was a disconnect between what the statistical methods required and the substantive information that could be brought to bear. In this paper, I briefly consider some alternatives to causal modeling assuming that David Freedman’s perspective on modeling is correct. In addition to randomized experiments and strong quasi-experiments, I discuss multivariate statistical analysis, exploratory data analysis, dynamic graphics, machine learning and knowledge discovery.

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    File URL: http://www.era.org.tr/makaleler/2050034.pdf
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    Bibliographic Info

    Article provided by Econometric Research Association in its journal International Econometric Review.

    Volume (Year): 1 (2009)
    Issue (Month): 1 (April)
    Pages: 18-27

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    Handle: RePEc:erh:journl:v:1:y:2009:i:1:p:18-27

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    Related research

    Keywords: Causal Modeling; Regression Analysis; Exploratory Data Analysis; Data Science;

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    References

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    1. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    2. Leeb, Hannes & Pötscher, Benedikt M., 2005. "Can One Estimate the Unconditional Distribution of Post-Model-Selection Estimators ?," MPRA Paper 72, University Library of Munich, Germany.
    3. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    4. James L. Heckman, 1999. "Causal Parameters and Policy Analysis in Economcs: A Twentieth Century Retrospective," NBER Working Papers 7333, National Bureau of Economic Research, Inc.
    5. Leeb, Hannes & P tscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(01), pages 21-59, February.
    6. D. A. Freedman & P. B. Stark & K. W. Wachter, 2001. "A probability model for census adjustment," Mathematical Population Studies, Taylor & Francis Journals, vol. 9(2), pages 165-180.
    7. Kenneth W. Wachter & David A. Freedman, 2000. "Measuring Local Heterogeneity with 1990 U.S. Census Data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 3(10), November.
    8. Klaus Nordhausen, 2009. "The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman," International Statistical Review, International Statistical Institute, vol. 77(3), pages 482-482, December.
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