Spring 2019 Schedule
The PML speaker series for AY 2018-19 will be in the Millikan Room (E53-482) on Mondays at 12-1:30pm. Lunch will be provided.
Friday, March 15, 2019: 12-1:30 p.m.
Title: "Ends Against the Middle: Introducing the Generalized Graded Unfolding Model for Non-monotonic Item Response Functions"
Abstract: Standard methods for measuring ideology from voting records assume strict monotonicity of responses in individuals’ latent traits. If this assumption holds, we should not observe instances where individuals at the extremes act together in opposition to moderates. In practice, however, there are many times when individuals from both extremes may behave identically but for opposing reasons. For example, both liberal and conservative justices may dissent from the same Supreme Court decision but provide ideologically contradictory reasons. In this paper, we introduce to the political science literature the generalized graded unfolding model (GGUM), first proposed by Roberts, Donoghue, and Laughlin (2000), which accommodates non-monotonic response functions consistent with single-peaked preferences. In addition to explaining the method, we provide a novel estimation method and software that outperforms existing routines. We then apply this method to voting data from the U.S. Supreme Court and Congress and show that the GGUM outperforms standard methods in terms of both predictive accuracy and substantive insights.
Wednesday, April 17, 2019: 12-1:30 p.m.
Title: "Credible or Confounded? Applying Sensitivity Analyses to Improve Research and its Evaluation under Imperfect Identification (with Francesca Parente)"
Abstract: 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).
Working paper: Here
For any questions or suggestions, please contact Teppei Yamamoto or pmlab-contact@mit.edu.