What Now? Some Brief Reflections on Model-Free Data Analysis
Year: 2009
Type: article
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.
Author: Richard Berk
Institution: University of Pennsylvania
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1596377546