Campbell and Rubin: A primer and comparison of their approaches to causal inference in field settings

This article compares Donald Campbell's and Donald Rubin's work on causal inference in field settings on issues of epistemology, theories of cause and effect, methodology, statistics, generalization, and terminology. The two approaches are quite different but compatible, differing mostly in matters of bandwidth versus fidelity. Campbell's work demonstrates broad narrative scope that covers a wide array of concepts related to causation, with a powerful appreciation for human fallibility in making causal judgments, with a more elaborate theory of cause and generalization, and with a preference for design over analysis. Rubin's approach is a more narrow and formal quantitative analysis of effect estimation, sharing a preference for design but best known for analysis, with compelling quantitative approaches to obtaining unbiased quantitative effect estimates from nonrandomized designs and with comparatively little to say about generalization. Much could be gained by joining the emphasis on design in Campbell with the emphasis on analysis in Rubin. However, the 2 approaches also speak modestly different languages that leave some questions about their total commensurability that only continued dialogue can fully clarify.