Publications

• "Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment"

  Kosuke Imai, Zhichao Jiang, James Greiner, Ryan Halen, and Sooahn Shin
  (with discussion), Journal of the Royal Statistical Society, Series A (Statistics in Society), 2023.

• "Bayesian Methods in Political Science"

  Jong Hee Park and Sooahn Shin
  Luigi Curini and Robert Franzese eds., The SAGE Handbook of Research Methods in Political Science & International Relations, 2020.

Working Papers

• "Does AI help humans make better decisions? A methodological framework for experimental evaluation"

  Eli Ben-Michael, D. James Greiner, Melody Huang, Kosuke Imai, Zhichao Jiang, and Sooahn Shin

• "Difference-in-differences Design with Outcomes Missing Not at Random"

  Sooahn Shin

• "Synthetic Control Methods with Pre-treatment Outcomes Missing"

  Naijia Liu, Sooahn Shin, and Soichiro Yamauchi

• "Measuring Issue Specific Ideal Points from Roll Call Votes"

  Sooahn Shin

• "ℓ1 norm Based Multidimensional Ideal Point Estimation"

  Sooahn Shin, Johan Lim, and Jong Hee Park
  Revise & Resubmit, JASA Applications and Case Studies

Teaching Fellow

• Gov 2003 Causal Inference with Applications, Fall 2023
  Prof. Kosuke Imai
  The third course of Core Government Methods Sequence for Graduates (course website)
• Gov 2017 Applied Bayesian Statistics for the Social Sciences, Spring 2023
  Prof. Kosuke Imai
  The fourth course of Core Government Methods Sequence for Graduates (syllabus)
• Gov 50 Data Science for the Social Sciences, Fall 2022
  Prof. Matthew Blackwell
  Undergraduate data science course (course website)
• Gov 2018 Introduction to Machine Learning, Spring 2022
  Prof. Adeline Lo
  The fourth course of Core Government Methods Sequence for Graduates
• Gov 2003 Causal Inference with Applications, Fall 2021
  Prof. Matthew Blackwell
  The third course of Core Government Methods Sequence for Graduates (section notes)

Software

• aihuman (Available on CRAN)

  R package written in Rcpp for an experimental evaluation of causal impacts of algorithmic recommendations on human decisions developed by Imai et al. (JRSS, 2023).

• issueirt

  R package for issue specific ideal point estimation developed by Shin (working paper). It uses Stan. Available upon request.

• l1ideal

  R package for ℓ1 norm based multidimensional ideal point estimation developed by Shin et al. (working paper). It uses multivariate slice sampling for the estimation.