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.
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.
I propose a method for drawing causal inferences using ordinal outcomes in the difference-in-differences setting.
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.
Naoki Egami and S. Yamauchi. Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs.
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.
Jong Hee Park and S. Yamauchi. Regularized Regression with Change-points: Introducing Hidden Markov Bayesian Bridge Model.
We develop a Bayesian regression model with shrinkage priors for analyzing longitudinal data with multiple change-points.
Diana Stanescu, Erik H. Wang, and S. Yamauchi. (Forthcoming). 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.
- 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.
Yamauchi. (2020+). EM Algorithm for Multinomial Logit.
Dynamic Plackett-Luce Model for Ranked Data: Application to the Rating of Formula One Drivers.