Soichiro Yamauchi


  1. Shiro Kuriwaki, Stephen Ansolabehere, Angelo Dagonel, and S. Yamauchi. 2023+. The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level. American Political Science Review, Forthcoming.

    We estimate vote choice by race at the Congressional District level using survey data.

    [supplemental material] [software]

  2. Jong Hee Park and S. Yamauchi. 2023. Change-Point Detection and Regularization in Time Series Cross-Sectional Data Analysis. Political Analysis 31(2): 257-277.

    We develop a Bayesian regression model with shrinkage priors for analyzing longitudinal data with multiple change-points.

  3. Naoki Egami and S. Yamauchi. 2023. Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs. Political Analysis, 31(2): 195-212.

    We propose an estimator for estimating the causal effect that improves upon the standard difference-in-differences design in terms of identification and estimation accuracy by exploiting multiple pre-treatment periods.

    [arXiv] [replication material] [software] [appendix]

  4. Diana Stanescu, Erik H. Wang, and S. Yamauchi. 2019. Using LASSO to Assist Imputation and Predict Child Wellbeing. Socius, 5:1--21.

    Final prize (the best score for material hardship) for Fragile Family Challenge.

    [supplemental material]


  1. Matthew Blackwell and S. Yamauchi. Adjusting for Unmeasured Confounding in Marginal Structural Models with Propensity-Score Fixed Effects.

    We propose a method to adjust for unmeasured time-invariant confounders in marginal structural models.

    [abstract] [slides]

  2. Shiro Kuriwaki and S. Yamauchi. Synthetic Area Weighting for Measuring Public Opinion in Small Areas.

    We propose a weighting method to estimate small area quantities.

    [abstract] [software]

  3. S. Yamauchi. Difference-in-Differences for Ordinal Outcomes: Application to the Effect of Mass Shootings on Attitudes towards Gun Control.

    I propose a method for drawing causal inferences using ordinal outcomes in the difference-in-differences setting.

    [abstract] [software] [slides]

  4. S. Yamauchi. Nonparametric Sensitivity Analysis for Randomized Experiments with Missing Outcomes.

    I propose a method to conduct a nonparametric sensitivity analysis and conduct inference for missing outcome in randomized experiments. The method provides bounds and confidence intervals for treatment effects that account for non-ignorable missing and attrition.