Erin Lipman | Searcharxiv

arXiv2024

Explaining Differences in Voting Patterns Across Voting Domains Using Hierarchical Bayesian Models

Spatial voting models of legislators' preferences are used in political science to test theories about their voting behavior. These models posit that legislators' ideologies as well as the ideologies reflected in votes for and against a bill or measure exist as points in some low dimensional space, and that legislators vote for positions that are close to their own ideologies. Bayesian spatial voting models have been developed to test sharp hypotheses about whether a legislator's revealed ideal point differs for two distinct sets of bills. This project extends such a model to identify covariates that explain whether legislators exhibit such differences in ideal points. We use our method to examine voting behavior on procedural versus final passage votes in the U.S. house of representatives for the 93rd through 113th congresses. The analysis provides evidence that legislators in the minority party as well as legislators with a moderate constituency are more likely to have different ideal points for procedural versus final passage votes.

stat.AP↗

arXiv2024

On Data Analysis Pipelines and Modular Bayesian Modeling

The most common approach to implementing data analysis pipelines involves obtaining point estimates from the upstream modules and then treating these as known quantities when working with the downstream ones. This approach is straightforward, but it is likely to underestimate the overall uncertainty associated with any final estimates. An alternative approach involves estimating parameters from the modules jointly using a Bayesian hierarchical model, which has the advantage of propagating upstream uncertainty into the downstream estimates. However, when modules are misspecified, such a joint model can behave in unexpected ways. Furthermore, hierarchical models require the development of ad-hoc computational implementations that can be laborious and computationally expensive. Cut inference modifies the posterior distribution to prevent information flow between certain parameters and provides a third alternative for statistical inference in data analysis pipelines. This paper presents a unified framework that encompasses two-step, cut, and joint inference in the context of data analysis pipelines with two modules and uses two examples to illustrate the tradeoffs associated with these approaches. Our work shows that cut inference provides both some level of robustness and ease of implementation for data analysis pipelines at a lower cost in terms of statistical inference.

stat.ME↗