As a side note, it is interesting to contrast the profound impact the approaches of Campbell and Rubin have had on empirical work in economics with that of the graphical approach. Although the graphical approach to causality has been around for more than 2 decades (Pearl, 1995, 2009a; Spirtes, Glymour, & Scheines, 2001), it has had virtually no impact on practice in economics. Whereas Pearl (2009b) appears to see this as a lack of open mindedness in economics, the fast and widespread adoption of aspects of Campbell’s work and Rubin’s approach suggests the willingness of economists to adopt new methods, as long as the benefits are transparent. My personal view is that the proponents of the graphical approach, unlike Campbell and Rubin, have not demonstrated convincingly to economists that adopting (part) of their framework offers sufficient benefits relative to the framework currently used by economists.
It’s been 15 years since I spent any time contemplating “the graphical approach.” At that time, I was far more interested in prediction than causal inference. (These are different in the sense that correlations that have nothing to do with causation can still be helpful for some kinds of prediction problems but, by definition, confound causal inference.) Recently it was suggested to me that I reconsider graphical models. Considering opportunity cost, Imbens’ “don’t bother” is highly influential, however.
Unconfoundedness implies that comparisons of outcomes for units that differ in terms of treatment status but are homogeneous in terms of observed covariates have a causal interpretation. In other words, if we find a pair of units with the same covariate values, one treated and one control, then the difference in outcomes is unbiased for the average effect of the treatment for units with those values of the covariates.
Let me make some comments on this, because the assumption has generated much more controversy in economics than one might expect. This is partly because the assumption that units that look alike in terms of observed characteristics but that are in different treatment regimes are directly comparable is often suspect. If these units look alike in terms of background characteristics, but they made different choices, it must be because they are, in fact, different in terms of unobserved characteristics. In other words, if they were the same in terms of all relevant characteristics, why would they make different choices? The underlying concern among economists is that such an assumption may be difficult to reconcile with optimal behavior by individuals.
This is precisely my beef with those who find convincing observational studies of insurance status (e.g., Medicaid vs. uninsured) that don’t control for selection on unobservables. Even controlling for a wide variety of observable characteristics, one is left with essentially the conundrum that Imbens raises. Why did two otherwise seemingly identical individuals make such different choices? Is it plausible that whatever caused them to do so (e.g., unobservable disease severity or community support) could also affect health outcomes that are the focus of such studies? Yes, it is. This is among the most useful ways economists think that many others do not.