Peer-reviewed Publications

  1. Explaining Legal Inconsistency

    Abstract Judges, scholars, and commentators decry inconsistent areas of judicially created policy. This could hurt courts’ policy making efficacy, so why do judges allow it to happen? I show judicially-created policy can become inconsistent when judges explain rules in more abstract terms than they decide cases. To do so, I expand standard case-space models of judicial decision making to account for relationships between specific facts and broader doctrinal dimensions. This model of judicial decision making as a process of multi-step reasoning reveals that preference aggregation in such a context can lead to inconsistent collegial rules. I also outline a class of preference configurations on collegial courts (i.e., multi-member courts) in which this problem cannot arise. These results have implications for several areas of inquiry in judicial politics such as models of principal-agent relationships in judicial hierarchies and empirical research utilizing case facts as predictor variables.

  2. Agenda Setting and Attention to Precedent in the US Federal Courts

    Abstract To what degree is judicial agenda setting top-down or bottom-up? Existing studies lack evidence of the frequency or magnitude of these two processes. We conceptualize the judicial agenda as the legal questions/rules receiving judicial attention, measure it using citations to Supreme Court opinions, and estimate vector autoregression models to identify how each level of court initiates or responds to variation in attention to precedent at other levels of the judiciary. The Supreme Court exerts some top-down control, but agenda setting is more often bottom-up, revealing lower courts are more integral to setting the federal judicial agenda than previously understood.

  3. Ends Against the Middle: Measuring Latent Traits When Opposites Respond the Same Way for Antithetical Reasons

    Abstract Standard methods for measuring latent traits from categorical data assume that response functions are monotonic. This assumption is violated when individuals from both extremes respond identically but for conflicting reasons. Two survey respondents may "disagree" with a statement for opposing motivations, liberal and conservative justices may dissent from the same Supreme Court decision but provide ideologically contradictory rationales, and in legislative settings, ideological opposites may join together to oppose moderate legislation in pursuit of antithetical goals. In this article, we introduce a scaling model that accommodates ends against the middle responses and provide a novel estimation approach that improves upon existing routines. We apply this method to survey data, voting data from the United States Supreme Court, and the 116th Congress, and show it outperforms standard methods in terms of both congruence with qualitative insights and model fit. This suggests that our proposed method may offer improved one-dimensional estimates of latent traits in many important settings.

  4. GPIRT: A Gaussian Process Model for Item Response Theory

    Abstract The goal of item response theoretic (IRT) models is to provide estimates of latent traits from binary observed indicators and at the same time to learn the item response functions (IRFs) that map from latent trait to observed response. However, in many cases observed behavior can deviate significantly from the parametric assumptions of traditional IRT models. Nonparametric IRT models overcome these challenges by relaxing assumptions about the form of the IRFs, but standard tools are unable to simultaneously estimate flexible IRFs and recover ability estimates for respondents. We propose a Bayesian nonparametric model that solves this problem by placing Gaussian process priors on the latent functions defining the IRFs. This allows us to simultaneously relax assumptions about the shape of the IRFs while preserving the ability to estimate latent traits. This in turn allows us to easily extend the model to further tasks such as active learning. GPIRT therefore provides a simple and intuitive solution to several longstanding problems in the IRT literature.

Working Papers