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Naoki Egami
Curriculum Vitae
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Email: naoki.egami at Columbia.Edu

Assistant Professor

Department of Political Science
Columbia University








I am an Assistant Professor in the Department of Political Science at Columbia University. I specialize in political methodology and develop statistical methods for questions in political science and the social sciences.

I am broadly interested in causal inference and machine learning methods for the social sciences. My research projects include external validity of causal findings and causal inference with network and spatial data. Please see Research for the overview of my research areas. My work has appeared or is forthcoming in various academic journals, such as American Political Science Review, American Journal of Political Science, Political Analysis, Journal of the American Statistical Association, Journal of the Royal Statistical Society (Series A), and Science Advances.

I have won several awards for my research. In 2022, my paper on causal peer effects won the Best Conference Paper Award from Political Networks Section in APSA. In 2019, my work on causal diffusion analysis won the Gosnell Prize from the Society for Political Methodology. In 2017, I also received the John T. Williams Dissertation Prize.

I received a Ph.D. from Princeton University (2020) and a B.A. from the University of Tokyo (2015). I was a pre-doctoral fellow in the Department of Government at Harvard University from 2018 to 2020. I also studied at the University of Michigan, Ann Arbor, as a visiting student in 2013.

Recent News

  • I am excited to share a new paper on site selection for multi-context (multi-site) studies. In this paper, we examine how researchers can select study sites for external validity, while accommodating practical and ethical constraints social scientists face. We offer the companion R package spsR and its associated website.
  • I am excited to share a new paper on large language models (LLMs). In this paper, we examine how researchers can efficiently use labels/annotations generated by LLMs in downstream statistical analyses, without introducing bias. Our new R package dsl is forthcoming!
  • I am excited to share a new paper on external robustness, which I co-authored with my student, Martin Devaux. In this paper, we consider a question of external robustness --- quantifying how robust an experiment is to external validity bias. R package exr is available here.
  • My paper on causal inference with text data is published at Science Advances. In this paper, we propose a framework of making causal inferences with text data.
  • My paper on external validity is published at the APSR. In this paper, we propose a formal framework of external validity that encompasses four dimensions; X-, T-, Y -, and C-validity (populations, treatments, outcomes, and contexts).
  • I am honored to be awarded the 2022 Best Conference Paper Award from the Political Networks Section of the APSA for my paper on causal peer effects.

Recent Invited Talks

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