Soichiro Yamauchi

Shiro Kuriwaki, Stephen Ansolabehere, Angelo Dagonel, and S. Yamauchi. 2024. The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level. American Political Science Review, 118(2): 922-939.

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

[supplemental material] [software]
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.
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]
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]

Naijia Liu, Sooahn Shin, and S. Yamauchi. Synthetic Control Methods with Missing Pre-treatment Outcomes.

We propose a method for assessing the robustness of the synthetic control methods when units are dropped from an analysis due to missing data.
Matthew Blackwell and S. Yamauchi. The Effect of Political Advertising after Citizens United: Adjusting for Unmeasured Confounding in Marginal Structural Models.

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

[previous version] [slides]
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]
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]
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.

[software]

S. Yamauchi. 2022. Essays on Political Methodology. PhD Dissertation, Harvard University.

Kuriwaki and Yamauchi. bmlogit: R package for implementing multinomial logistic regression with prediction constraints.
Yamauchi. attritionCI: R package for constructing sensitivity-aware confidence interval for causal effect estimates under nonignorable missingness in the outcome. The method is proposed in Yamauchi (2021).
Egami and Yamauchi. DIDdesign: R package for implementing the double difference-in-differences method proposed in Egami and Yamauchi (2019).
Yamauchi. dyRank: R package for implementing the hierarchical dynamic rating model for estimating the dynamic rating with rank-ordered data.
Yamauchi. emlogit: R package for implementing the multinomial logistic regression based on the Expectation and Conditional Maximization (ECM) algorithm.
Yamauchi. orddid: R package for implementing the difference-in-differences for ordinal outcomes proposed in Yamauchi (2020+).
Park and Yamauchi. BridgeChange: R package for implementing the Bayesian regularization regression with multiple change points for time-series and panel data.