Naoki Egami is a Ph.D. candidate in the Department of Politics at Princeton University and a pre-doctoral fellow in the Department of Government at Harvard University. His work received the John T. Williams Dissertation Prize in 2017 from the Society for Political Methodology (for the best dissertation proposal in the area of political methodology). He obtained a B.A. in Liberal Arts from the University of Tokyo in 2015, and also studied at University of Michigan, Ann Arbor, as a visiting student in 2013.
Egami is broadly interested in political methodology and comparative political behavior. His research has focused on spatial and network causal inference and the development of machine learning methods for the social sciences.
Egami, Naoki and Kosuke Imai. 2018. “Causal Interaction in Factorial Experiments: Application to Conjoint Analysis.” Journal of the American Statistical Association.
Egami, Naoki. “Identification of Causal Diffusion Effects Using Stationary Causal Directed Acyclic Graphs.” Working Paper.
Egami, Naoki. “Unbiased Estimation and Sensitivity Analysis for Network-Specific Spillover Effects: Application to An Online Network Experiment.” Working Paper.
Egami, Naoki, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart. “How to Make Causal Inferences Using Texts.” Working Paper.
Chou, Winston, Rafaela Dancygier, Naoki Egami, and Amaney Jamal. “The Illusion of Far-Right Partisan Stability: How Party Positioning Affects Far-Right Voting in Germany.” Working Paper.
Selected Honors and Awards
John T. Williams Dissertation Prize, the Society for Political Methodology, 2017
George Kateb Preceptor Award, Princeton University, 2018.