（学）：学部生
（修）：大学院修士課程（・博士課程）
How to Write an Empirical Social Science Paper by Kosuke Imai
How to Write the Introduction of Your Development Economics Paper by David Evans
Navigating an R & R by APSR Editors
2024年度春学期．東京大学大学院法学政治学研究科．因果推論
2023年度秋学期．一橋大学大学院ソーシャル・データサイエンス研究科．政治学の実証分析（因果推論）
2020年度秋学期．学習院大学大学院政治学研究科．統計分析II（因果推論の基礎）
（修）Imbens, Guido W. and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.
（修）Angrist, Joshua D. and Jorn-steffen Pischke. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. 和訳版：『「ほとんど無害」な計量経済学：応用経済学のための実証分析ガイド』．NTT出版．
（修）Morgan, Stephen L. and Christopher Winship. 2014. Counterfactuals and Causal Inference: Methods and Principles for Social Research (second edition). Cambridge University Press.
（修）Hernán, MA and Robins JM. 2020. Causal Inference: What If. Chapman & Hall/CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
（学）松林哲也．2021．『政治学と因果推論：比較から見える政治と社会』．岩波書店．
（修）Abadie, Alberto, and Matias D. Cattaneo. 2018. "Econometric Methods for Program Evaluation." Annual Review of Economics 10: 465--503.
（修）Brand, Jennie E., Xiang Zhou, and Yu Xie. 2023. "Recent Developments in Causal Inference and Machine Learning." Annual Review of Sociology 49: 81--110.
（修）Imbens, Guido W., and Jeffrey M. Wooldridge. 2009. "Recent Developments in the Econometrics of Program Evaluation." Journal of Economic Literature 47 (1): 5--86.
（学）今井耕介．2018．『社会科学のためのデータ分析入門（上・下）』．岩波書店．
（学）Hansen, Bruce E. 2022. Probability and Statistics for Economists. Princeton University Press.
（学）Wooldridge, Jeffrey M. 2020. Introductory Econometrics: A Modern Approach (seventh edition). Cengage.
（修）Hansen, Bruce E. 2022. Econometrics. Princeton University Press.
（修）末石直也．2015．『計量経済学：ミクロデータ分析へのいざない』．日本評論社．
（修）Wooldridge, Jeffrey M. 2010. Econometric Analysis of Cross Section and Panel Data (second edition). The MIT Press.
（修）Hayashi, Fumio. 2000. Econometrics. Princeton University Press.
（学）Gelman, Andrew and Jennifer Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
（修）Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin. 2014. Bayesian Data Analysis, 3rd ed. Chapman and Hall/CRC.
（修）Hoff, Peter D. 2009. A First Course in Bayesian Statistical Methods. Springer. 和訳版：『標準 ベイズ統計学』．朝倉書店．
（学）久保拓弥．2012．『データ解析のための統計モデリング入門：一般化線形モデル・階層ベイズモデル・MCMC』．岩波書店．
（修）Bishop, Christopher M. 2007. Pattern Recognition and Machine Learning. Springer (A great introduction to machine learning).
（修）Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning. Springer.
（修）Murphy, Kevin P. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press
（学）Gareth, James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2014. An Introduction to Statistical Learning: with Applications in R. Springer.
（修）Efron, Bradley and Trevor Hastie. 2016. Computer Age Statistical Inference: Algorithms, Evidence and Data Science. Cambridge University Press.
Chernozhukov, Victor, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis. Applied Causal Inference Powered by ML and AI. causalml-book.org
Athey, Susan and Guido W. Imbens. 2019. "Machine Learning Methods That Economists Should Know About." Annual Review of Economics 11: 685--725.
Brand, Jennie E., Xiang Zhou, and Yu Xie. 2023. "Recent Developments in Causal Inference and Machine Learning." Annual Review of Sociology 49: 81--110.
Varian, Hal R. 2014. "Big Data: New Tricks for Econometrics." Journal of Economic Perspectives 28 (2): pp.3--27.
Belloni, Alexandre, Victor Chernozhukov, and Christian Hansen. 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects." Journal of Economic Perspectives 28 (2): 29--50.
Athey, Susan. 2018. "The Impact of Machine Learning on Economics." The Economics of Artificial Intelligence: An Agenda. University of Chicago Press: 507--47.
Mullainathan, Sendhil and Jann Spiess. 2017. "Machine Learning: An Applied Econometric Approach." Journal of Economic Perspectives 31 (2): 80--106.
（学）椎名洋，姫野哲人，保科架風．2019．『データサイエンスのための数学』．講談社．1，2，7--14章．
（学）Hansen, Bruce E. 2022. Probability and Statistics for Economists. Princeton University Press.
（学）今井耕介．2018．『社会科学のためのデータ分析入門（上・下）』．岩波書店．（特に6--7章）
（学）尾山大輔，安田洋祐．2013．『経済学で出る数学：高校数学からきちんと攻める』．日本評論社．（特に8--10章）
（修）神谷和也，浦井憲．1996．『経済学のための数学入門』．東京大学出版会．
（修）田中久稔．2019．『計量経済学のための数学』．日本評論社．
（学）岩澤政宗．2023．『計量経済学のための統計学』．日本評論社．
（学）Hansen, Bruce E. 2022. Probability and Statistics for Economists. Princeton University Press.
（学）東京大学教養学部統計学教室編．1991．『統計学入門』．東京大学出版会．
（学）久保川達也．2023．『データ解析のための数理統計入門』．共立出版．
（修）久保川達也．2017．『現代数理統計学の基礎』．共立出版．
1. Wickham, Hadley, Mine Cetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Second edition). O'Reilly Media.
著者HPの無料版：https://r4ds.hadley.nz
2. Chang, Winston. 2020. R Graphics Cookbook. O'Reilly Media.
和訳版：https://www.oreilly.co.jp/books/9784873118925/
著者HPの無料版：https://r-graphics.org
3. 今井耕介．2018．『社会科学のためのデータ分析入門（上・下）』．岩波書店．
和訳版：https://www.iwanami.co.jp/book/b352363.html
原著版：http://qss.princeton.press
1. ggplot2 cheat sheet: https://rstudio.com/wp-content/uploads/2016/10/ggplot2-cheatsheet-2.0-ja.pdf
2. The tidyverse style guide: https://style.tidyverse.org
3. R Markdown: Get Started: rmarkdown.rstudio.com/lesson-1.html
4. The viridis color palettes: https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html
5. Fundamentals of Data Visualization by Claus O. Wilke (O’Reilly Media)
著者HPの無料版：https://serialmentor.com/dataviz/