Credible or Confounded? Applying Sensitivity Analyses to Improve Research and its Evaluation under Imperfect Identification (with Francesca Parents)

April 17, 2019 12:00PM E53-482

Social scientists pose important questions about the effects of potential causes, but often cannot eliminate all possible confounders in defense of causal claims. Sensitivity analyses can be useful in these circumstances, providing a route to rigorously investigate causal questions despite imperfect identification. Further, if more widely adopted, these tools have the potential to improve upon standard practice for communicating the robustness causal claims, while suggesting new ways for readers and reviewers to judge research. We illustrate these uses of sensitivity analysis in an application that examines two potential causes of support for the 2016 Colombian referendum for peace with the FARC. Conventional regression analyses find "statistically and substantively significant" estimated effects for both causes. Yet, sensitivity analyses reveal very weak confounders could overturn one cause (exposure to violence), but extremely powerful confounders are needed to overturn the other (political affiliation with the deal's champion).

Political Methodology Speaker Series