Research Interest
- Political Methodology
- Causal Inference
Contact Information
sooahnshin_at_g.harvard.eduSooahn Shin
Sooahn Shin
I'm a PhD candidate in the Department of Government at Harvard University and an affiliate of the Institute for Quantitative Social Science (IQSS). My research interests include political methodology and causal inference.
My current research programs focus on developing methods for (1) measuring ideological scores beyond a single-dimensional scale, (2) assessing decision-making systems with algorithmic recommendations, and (3) addressing bias from missing values when estimating causal effects using panel data.
I recieved the John T. Williams dissertation prize from the Society for Political Methodology for "the best dissertation proposal in the area of political methodology."
Publications
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"ℓ1-based Bayesian Ideal Point Model for Multidimensional Politics"
Journal of the American Statistical Association, Forthcoming. -
"Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment"
(with discussion), Journal of the Royal Statistical Society, Series A (Statistics in Society), 2023. -
"Bayesian Methods in Political Science"
Luigi Curini and Robert Franzese eds., The SAGE Handbook of Research Methods in Political Science & International Relations, 2020.
Working Papers
Ideal Point Estimation Beyond a Single Dimension
Impact of AI on Human Decisions
Causal Inference Using Panel Data with Missingness
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"Difference-in-differences Design with Outcomes Missing Not at Random"
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"Synthetic Control Methods with Missing Pre-treatment Outcomes"
Teaching Fellow
At Harvard, I served as a teaching fellow for PhD-level courses in causal inference, Bayesian statistics, and machine learning, all part of the Government Department Methods Sequence.
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Causal Inference with Applications, Fall 2023 [course website]
Gov 2003,
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Applied Bayesian Statistics for the Social Sciences, Spring 2023 [syllabus]
Gov 2017,
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Data Science for the Social Sciences, Fall 2022 [course
website]
Gov 50 (undergraduate),
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Introduction to Machine Learning, Spring 2022
Gov 2018,
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Causal Inference with Applications, Fall 2021 [section notes]
Gov 2003,
Software
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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. -
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.