Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/238781 
Authors: 
Year of Publication: 
2009
Citation: 
[Journal:] International Econometric Review (IER) [ISSN:] 1308-8815 [Volume:] 1 [Issue:] 1 [Publisher:] Econometric Research Association (ERA) [Place:] Ankara [Year:] 2009 [Pages:] 18-27
Publisher: 
Econometric Research Association (ERA), Ankara
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.
Subjects: 
Causal Modeling
Regression Analysis
Exploratory Data Analysis
Data Science
JEL: 
C81
C50
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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