Research

Peer-Reviewed Publications


  1. Chiou, Fang-Yi and Max Goplerud. 2024+. "Effective Lawmaking Across Congressional Eras." The Journal of Politics. Advance Access. [publisher's version] [preprint] [replication data]

  2. Chang, Qing, and Max Goplerud. 2023+. "Generalized Kernel Regularized Least Squares." Political Analysis. Advance Access. [publisher's version] [preprint] [R package] [replication data]

  3. Goplerud, Max. 2024. "Re-Evaluating Machine Learning for MRP Given the Comparable Performance of (Deep) Hierarchical Models." American Political Science Review. 118(1):529-536. [publisher's version] [preprint] [R package] [replication data]

  4. Goplerud, Max, and Daniel M. Smith. 2023. "Who Answers for the Government? Bureaucrats, Ministers, and Responsible Parties." American Journal of Political Science. 67(4):963-978. [publisher's version] [preprint] [replication data]

  5. Goplerud, Max. 2022. "Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference." Bayesian Analysis. 17(2):632-650. [publisher's version] [preprint] [R package] [replication data]

  6. Fernandes, Jorge, Max Goplerud, and Miguel Won. 2019. "Legislative Bellwethers: The Role of Committee Membership in Parliamentary Debate." Legislative Studies Quarterly. 44(2):307-343. [publisher's version] [preprint] [appendix] [replication data]

  7. Goplerud, Max. 2019. "A Multinomial Framework for Ideal Point Estimation." Political Analysis. 27(1):69-89. [publisher's version] [preprint] [appendix] [replication data]

  8. Goplerud, Max, and Torben Iversen. 2018. "Redistribution Without A Median Voter: Models of Multidimensional Politics." Annual Review of Political Science. 21:295-317. [publisher's version]

  9. Goplerud, Max. 2016. "Crossing the Boundaries: An Implementation of Two Methods for Projecting Data across Boundary Changes." Political Analysis. 24(1):121-129. [publisher's version] [replication data]

  10. Goplerud, Max, and Petra Schleiter. 2016. "An Index of Assembly Dissolution Powers." Comparative Political Studies. 49(4):427-456. [publisher's version] [replication data]

  11. Goplerud, Max. 2015. "The First Time is (Mostly) the Charm: Special Advisers as Parliamentary Candidates and Members of Parliament." Parliamentary Affairs. 68(2):332-351. [publisher's version]

 

Book Chapters and Other Publications


  1. Goplerud, Max, and James Bisbee. 2023. "BARP: Improving Mister P Using Bayesian Additive Regression Trees - Corrigendum." American Political Science Review. 117(2):785-787. [publisher's version]

  2. Goplerud, Max. 2022. "Methods for Analyzing Parliamentary Debates" and (with David A. Gelman) "Legislative Debates in the US Congress" in The Politics of Legislative Debates, (eds.) Hanna B├Ąck, Marc Debus, and Jorge Fernandes. Oxford University Press. [publisher's version]

  3. Goplerud, Max. 2014. "Appendix 1: Methodology" and "Appendix 2: Further Work on the Distribution and Tenure of Special Advisers" in Special Advisers: Who They Are, What They Do and Why They Matter, (eds.) Ben Yong and Robert Hazell. Oxford: Hart. [data on special advisers, 1979-2013]



Working Papers

  1. Goplerud, Max, Omiros Papaspiliopoulos, and Giacomo Zanella. "Partially Factorized Variational Inference for High-Dimensional Mixed Models."
    While generalized linear mixed models (GLMMs) are a fundamental tool in applied statistics, many specifications --- such as those involving categorical factors with many levels or interaction terms --- can be computationally challenging to estimate due to the need to compute or approximate high-dimensional integrals. Variational inference (VI) methods are a popular way to perform such computations, especially in the Bayesian context. However, naive VI methods can provide unreliable uncertainty quantification. We show that this is indeed the case in the GLMM context, proving that standard VI (i.e. mean-field) dramatically underestimates posterior uncertainty in high-dimensions. We then show how appropriately relaxing the mean-field assumption leads to VI methods whose uncertainty quantification does not deteriorate in high-dimensions, and whose total computational cost scales linearly with the number of parameters and observations. Our theoretical and numerical results focus on GLMMs with Gaussian or binomial likelihoods, and rely on connections to random graph theory to obtain sharp high-dimensional asymptotic analysis. We also provide generic results, which are of independent interest, relating the accuracy of variational inference to the convergence rate of the corresponding coordinate ascent variational inference (CAVI) algorithm for Gaussian targets. Our proposed partially-factorized VI (PF-VI) methodology for GLMMs is implemented in the R package vglmer. Numerical results with simulated and real data examples illustrate the favourable computation cost versus accuracy trade-off of PF-VI.
  2. Goplerud, Max, Kosuke Imai, and Nicole E. Pashley. "Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis."
    Estimation of heterogeneous treatment effects is an active area of research in causal inference. Most of the existing methods, however, focus on estimating the conditional average treatment effects of a single, binary treatment given a set of pre-treatment covariates. In this paper, we propose a method to estimate the heterogeneous causal effects of high-dimensional treatments, which poses unique challenges in terms of estimation and interpretation. The proposed approach is based on a Bayesian mixture of regularized regressions to identify groups of units who exhibit similar patterns of treatment effects. By directly modeling cluster membership with covariates, the proposed methodology allows one to explore the unit characteristics that are associated with different patterns of treatment effects. Our motivating application is conjoint analysis, which is a popular survey experiment in social science and marketing research and is based on a high-dimensional factorial design. We apply the proposed methodology to the conjoint data, where survey respondents are asked to select one of two immigrant profiles with randomly selected attributes. We find that a group of respondents with a relatively high degree of prejudice appears to discriminate against immigrants from non-European countries like Iraq. An open-source software package is available for implementing the proposed methodology.
  3. Goplerud, Max. "Modelling Heterogeneity Using Bayesian Structured Sparsity."
    How to estimate heterogeneity, e.g. the effect of some variable differing across observations, is a key question in political science. Methods for doing so make simplifying assumptions about the underlying nature of the heterogeneity to draw reliable inferences. This paper allows a common way of simplifying complex phenomenon (placing observations with similar effects into discrete groups) to be integrated into regression analysis. The framework allows researchers to (i) use their prior knowledge to guide which groups are permissible and (ii) appropriately quantify uncertainty. The paper does this by extending work on "structured sparsity" from a traditional penalized likelihood approach to a Bayesian one by deriving new theoretical results and inferential techniques. It shows that this method outperforms state-of-the-art methods for estimating heterogeneous effects when the underlying heterogeneity is grouped and more effectively identifies groups of observations with different effects in observational data.
  4. Goplerud, Max, Shiro Kuriwaki, Marc Ratkovic, and Dustin Tingley. "Sparse Multilevel Regression (and Poststratification (sMRP))." [draft]
    Multilevel models have long played an important role in a variety of social sciences. We extend this framework by bring to bear recent developments in the machine learning literature to allow for considerable flexibility. We introduce a sparse regression framework that covers both the linear case as well as a logit model for binary outcome data. We leverage recent computational tricks based on data-augmentation to dramatically speed up estimation times with equal or better performance compared to existing approaches. We apply our model in the context of multilevel modelling with post-stratification which has become a common tool for survey researchers.