*"If a “revolution” in our field or area of knowledge was ongoing, would we feel it and recognize it? And if so, how?...The “revolution” is partly founded on complex mathematics, and concepts as “counterfactuals,” as well as on attractive “causal diagrams” like Directed Acyclic Graphs (DAGs). Causal diagrams are a simple way to encode our subject-matter knowledge, and our assumptions, about the qualitative causal structure of a problem. Causal diagrams also encode information about potential associations between the variables in the causal network. DAGs must be drawn following rules much more strict than the informal, heuristic graphs that we all use intuitively. Amazingly, but not surprisingly, the new approaches provide insights that are beyond most methods in current use......The possible existence of a “revolution” might also be assessed in recent and new terms as collider, M-bias, causal diagram, backdoor (biasing path), instrumental variable, negative controls, inverse probability weighting, identifiability, transportability, positivity, ignorability, collapsibility, exchangeable, g-estimation, marginal structural models, risk set, immortal time bias, Mendelian randomization, nonmonotonic, counterfactual outcome, potential outcome, sample space, or false discovery rate."*

There is a lot said there. Most economists find themselves at home in relation to discussions involving most of this including anything related to potential outcomes and counterfactuals and the methods like those mentioned in the last paragraph. However, what might seem to make the revolution in epidemiology different from econometrics (at least for some applied economists) is the emphasis on directed acyclic graphs (DAGs).

Over at the Causal Analysis in Theory and Practice blog in a post titled "are economists smarter than epidemiologists (comments on imbens' recent paper)" they discuss comments by Guido Imbens from a statistical science paper (worth a read)

*"In observational studies in social science, both these assumptions tend to be controversial. In this relatively simple setting, I do not see the causal graphs as adding much to either the understanding of the problem, or to the analyses."*

The blog post is quite critical of this stance:

*"Can economists do in their heads what epidemiologists observe in their graphs? Can they, for instance, identify the testable implications of their own assumptions? Can they decide whether the IV assumptions (i.e., exogeneity and exclusion) are satisfied in their own models of reality? Of course the can’t; such decisions are intractable to the graph-less mind....Or, are problems in economics different from those in epidemiology? I have examined the structure of typical problems in the two fields, the number of variables involved, the types of data available, and the nature of the research questions. The problems are strikingly similar."*

Being trained in both biostatistics and econometrics, I encountered the credibility revolution and causal analysis mostly through seminars and talks on applied econometrics. As economist Jayson Lusk puts it:

*"if you attend a research seminar in virtually any economics department these days, you're almost certain to hear questions like, "what is your identification strategy?" or "how did you deal with endogeneity or selection?" In short, the question is: how do we know the effects you're reporting are causal effects and not just correlations."*

The first applications I encountered utilizing DAGs were either from economist Marc Bellemare with regard to one of his papers related to lagged explanatory variables, or it was a from a Statistics in Medicine paper authored by Davey Smith et al featuring Mendelian randomization.

**See also:**

How is it that SEMs subsume potential outcomes?

Mediators and moderators

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