Peer-Reviewed Publications

  1. Goplerud, Max. 2023+. "Re-Evaluating Machine Learning for MRP Given the Comparable Performance of (Deep) Hierarchical Models." American Political Science Review. Advance Access. [publisher's version] [preprint] [R package] [replication data]

  2. Goplerud, Max and Daniel M. Smith. 2021+. "Who Answers for the Government? Bureaucrats, Ministers, and Responsible Parties." American Journal of Political Science. Advance Access. [publisher's version] [preprint] [replication data]

  3. 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]

  4. 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]

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

  6. 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]

  7. 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]

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

  9. 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. Advance Access. [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. Chang, Qing and Max Goplerud. "Generalized Kernel Regularized Least Squares." Revise and Resubmit.
    Kernel Regularized Least Squares (KRLS) is a popular method for flexibly estimating models that may have complex relationships between variables. However, its usefulness to many researchers is limited for two reasons. First, existing approaches are inflexible and do not allow KRLS to be combined with theoretically-motivated extensions such as fixed effects or non-linear outcomes. Second, estimation is extremely computationally intensive for even modestly sized datasets. Our paper addresses both concerns by introducing generalized KRLS (gKRLS). We note that KRLS can be re-formulated as a hierarchical model thereby allowing easy inference and modular model construction. Computationally, we also implement random sketching to dramatically accelerate estimation while incurring a limited penalty in estimation quality. We demonstrate that gKRLS can be fit on datasets with tens of thousands of observations in under one minute. Further, state-of-the-art techniques that require fitting the model over a dozen times (e.g. meta-learners) can be estimated quickly.
  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. Chiou, Fang-Yi and Max Goplerud. "Measuring and Theorizing the Legislative Records of Members of the United States Congress, 1873-2010." Revise and Resubmit.
    A key concept in American legislative politics is the ability of members to build records of legislative accomplishment by proposing and advancing bills on important topics that garner substantial attention in the legislature. We contribute to this literature by highlighting theoretical links between legislative organization and a member’s legislative record. We propose a new measure of a member's legislative record that covers both chambers from 1873 to 2010 by incorporating all introduced bills (i.e., about 1.1 million bills). Empirically, we examine how its determinants vary and hinge on legislative institutions. Among our findings, we uncover that ideological moderates have stronger records relative to extremists particularly in the pre-1947 period, whereas majority-party membership is important only in the post-1947 period. We also find that institutional roles such as committee chairmanship are associated with stronger records throughout and their importance varies as legislative organization changes.
  4. 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.
  5. 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.