Teppei Yamamoto
Associate Professor of Political Science
Political methodology; applied statistics; causal inference; survey methodology; experimental design; political behavior.
Biography
Teppei Yamamoto is the Alfred Henry and Jean Morrison Hayes Career Development Associate Professor of Political Science at MIT. He obtained a BA in Liberal Arts from the University of Tokyo (2006) and an MA (2008) and PhD (2011) in Politics from Princeton University, where he received a Charlotte Elizabeth Procter Fellowship for the year of 2010 to 2011. His doctoral dissertation won the John T. Williams Dissertation Prize in 2010 from the Society for Political Methodology. He is broadly interested in developing quantitative methods for political science research, with particular focus on causal inference and experimental design. His research has appeared in various journals such as American Journal of Political Science and American Political Science Review.
Research
Professor Yamamoto is broadly interested in the development of quantitative methods for political science data. His research has focused on statistical methods for causal inference, including causal attribution, causal mediation, causal moderation, and causal inference with measurement error. I also study experimental designs and survey methodology, with empirical applications to elections and comparative political behavior.
Recent Publications
"Validating Vignette and Conjoint Survey Experiments against Real-World Behavior" (2015), Proceedings of the National Academy of Sciences of the United States of America, 112(8), 2395-2400 (with Jens Hainmueller and Dominik Hangartner).
"Identifying Mechanisms behind Policy Interventions via Causal Mediation Analysis" (2015), Journal of Policy Analysis and Management, 34(4), 937-963 (with Luke Keele and Dustin Tingley).
"Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments" (2014), Political Analysis, 22(1), 1-30 (with Jens Hainmueller and Dan Hopkins). Lead article.
Winner of the Warren Miller Prize and Editors' Choice award.
"mediation: R Package for Causal Mediation Analysis" (2014), Journal of Statistical Software, 59(5), 1--38 (with Dustin Tingley, Kentaro Hirose, Luke Keele and Kosuke Imai).
"Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments" (2013), Political Analysis, 21(2), 141-171 (with Kosuke Imai).
"Experimental Designs for Identifying Causal Mechanisms" (2013), Journal of the Royal Statistical Society, Series A, 176(1), 5--51 (with Kosuke Imai and Dustin Tingley). Lead article with discussions. Read before the Royal Statistical Society in 2012.
"Understanding the Past: Statistical Analysis of Causal Attribution" (2012), American Journal of Political Science, 56(1), 237-256.
"Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies." (2011) American Political Science Review, 105(4), pp.765-789 (with Kosuke Imai, Luke Keele and Dustin Tingley).
"Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis" (2010), American Journal of Political Science, 54(2), 543-560 (with Kosuke Imai).
"Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects" (2010), Statistical Science, 25(1), 51-71 (with Kosuke Imai and Luke Keele).
Teaching
| 17.871 | (LAB) Political Science Laboratory |
| 17.800 | Quantitative Research Methods I: Regression |
| 17.802 | Quantitative Research Methods II: Causal Inference |
| 17.804 | Quantitative Research Methods III: Generalized Linear Models and Extensions |
News
Biography
Teppei Yamamoto is the Alfred Henry and Jean Morrison Hayes Career Development Associate Professor of Political Science at MIT. He obtained a BA in Liberal Arts from the University of Tokyo (2006) and an MA (2008) and PhD (2011) in Politics from Princeton University, where he received a Charlotte Elizabeth Procter Fellowship for the year of 2010 to 2011. His doctoral dissertation won the John T. Williams Dissertation Prize in 2010 from the Society for Political Methodology. He is broadly interested in developing quantitative methods for political science research, with particular focus on causal inference and experimental design. His research has appeared in various journals such as American Journal of Political Science and American Political Science Review.
Research
Professor Yamamoto is broadly interested in the development of quantitative methods for political science data. His research has focused on statistical methods for causal inference, including causal attribution, causal mediation, causal moderation, and causal inference with measurement error. I also study experimental designs and survey methodology, with empirical applications to elections and comparative political behavior.
Recent Publications
"Validating Vignette and Conjoint Survey Experiments against Real-World Behavior" (2015), Proceedings of the National Academy of Sciences of the United States of America, 112(8), 2395-2400 (with Jens Hainmueller and Dominik Hangartner).
"Identifying Mechanisms behind Policy Interventions via Causal Mediation Analysis" (2015), Journal of Policy Analysis and Management, 34(4), 937-963 (with Luke Keele and Dustin Tingley).
"Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments" (2014), Political Analysis, 22(1), 1-30 (with Jens Hainmueller and Dan Hopkins). Lead article.
Winner of the Warren Miller Prize and Editors' Choice award.
"mediation: R Package for Causal Mediation Analysis" (2014), Journal of Statistical Software, 59(5), 1--38 (with Dustin Tingley, Kentaro Hirose, Luke Keele and Kosuke Imai).
"Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments" (2013), Political Analysis, 21(2), 141-171 (with Kosuke Imai).
"Experimental Designs for Identifying Causal Mechanisms" (2013), Journal of the Royal Statistical Society, Series A, 176(1), 5--51 (with Kosuke Imai and Dustin Tingley). Lead article with discussions. Read before the Royal Statistical Society in 2012.
"Understanding the Past: Statistical Analysis of Causal Attribution" (2012), American Journal of Political Science, 56(1), 237-256.
"Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies." (2011) American Political Science Review, 105(4), pp.765-789 (with Kosuke Imai, Luke Keele and Dustin Tingley).
"Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis" (2010), American Journal of Political Science, 54(2), 543-560 (with Kosuke Imai).
"Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects" (2010), Statistical Science, 25(1), 51-71 (with Kosuke Imai and Luke Keele).
Teaching
| 17.871 | (LAB) Political Science Laboratory |
| 17.800 | Quantitative Research Methods I: Regression |
| 17.802 | Quantitative Research Methods II: Causal Inference |
| 17.804 | Quantitative Research Methods III: Generalized Linear Models and Extensions |