DRAFT What You Can Learn From Wrong Causal Models

It is common for social science researchers to provide estimates of causal e↵ects from regression models imposed on observational data. The many problems with such work are well documented and widely known. The usual response is to claim, with little real evidence, that the causal model is close enough to the “truth” that su ciently accurate causal e↵ects can be estimated. In this chapter, a more circumspect approach is taken. We assume that the causal model is a substantial distance from the truth and then consider what can be learned nevertheless. To that end, we distinguish between how nature generated the data, a “true” model representing how this was accomplished, and a working model that is imposed on the data. The working model will typically be “wrong.” Nevertheless, unbiased or asymptotically unbiased estimates from parametric, semiparametric, and nonparametric working models can often be obtained in concert with appropriate statistical tests and confidence intervals. However, the estimates are not of the regression parameters typically assumed. Estimates of causal e↵ects are not provided. Correlation is not causation. Nor is partial correlation, even when dressed up as regression coe cients. However, we argue that insights about causal e↵ects do not require estimates of causal e↵ects. We also discuss what can be learned when our alternative approach is not persuasive.