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

Richard Berk
Additional contact information
Richard Berk: University of Pennsylvania

International Econometric Review (IER), 2009, vol. 1, issue 1, 18-27

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.

Keywords: Causal Modeling; Regression Analysis; Exploratory Data Analysis; Data Science (search for similar items in EconPapers)
JEL-codes: C50 C81 (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.era.org.tr/makaleler/2050034.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:erh:journl:v:1:y:2009:i:1:p:18-27

Access Statistics for this article

International Econometric Review (IER) is currently edited by Asad Zaman

More articles in International Econometric Review (IER) from Econometric Research Association Contact information at EDIRC.
Bibliographic data for series maintained by M. F. Cosar ().