Research

Max Goplerud

Peer-Reviewed Publications


  1. Goplerud, Max, Kosuke Imai, and Nicole E. Pashley. 2025. "Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis.'' Annals of Applied Statistics. 19(2): 866-888. [publisher's version] [preprint]
    [R package] [replication data]

  2. Goplerud, Max, Omiros Papaspiliopoulos, and Giacomo Zanella. 2025. "Partially Factorized Variational Inference for High-Dimensional Mixed Models." Biometrika. 111(2): XX-XX. [publisher's version] [preprint] [R package] [replication data]

  3. Chiou, Fang-Yi, and Max Goplerud. 2024. "Effective Lawmaking Across Congressional Eras." The Journal of Politics. 86(3):997-1012. [publisher's version] [preprint] [replication data]

  4. Chang, Qing, and Max Goplerud. 2024. "Generalized Kernel Regularized Least Squares." Political Analysis. 32(2):157-171. [publisher's version] [preprint] [R package] [replication data]

  5. 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]

  6. 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]

  7. 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]

  8. 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]

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

  10. 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]

  11. 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]

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

  13. 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 Bäck, 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. "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.
  2. 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.