What Now? Some Brief Reflections on Model-Free Data Analysis
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 c...
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| Format: | Article |
| Language: | English |
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Econometric Research Association
2009-04-01
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| Series: | International Econometric Review |
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| Online Access: | http://www.era.org.tr/makaleler/2050034.pdf |
| Summary: | 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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| ISSN: | 1308-8793 1308-8815 |