少しずつ埋めていきます
因果推論全般
- Miguel A. Hernán and James M. Robins. 2020. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. [Amazon] [Web]
- Scott Cunningham. 2021. Causal Inference: The Mixtape. Yale University Press. [Amazon] [Web]
- Guido W. Imbens and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. [Amazon]
- Stephen L. Morgan and Christopher Winship. 2014. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge University Press. [Amazon]
- Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell. 2016. Causal Inference in Statistics: A Primer. Wiley. [Amazon]
- 『入門 統計的因果推論』[Amazon]
- 安井翔太. 2020.『効果検証入門』技術評論社 [Amazon]
- Thad Dunning. 2008. "Improving Causal Inference: Strengths and Limitations of Natural Experiments." Political Research Quarterly. 61 (2): 282-293 [Sage]
Potential Outcome Framework
- Francesca Dominici, Falco J. Bargagli-Stoffi, and Fabrizia Mealli. 2020. "From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework." arXiv:2012.06865 [arXiv]
DAG
- Peter W G Tennant, Eleanor J Murray, Kellyn F Arnold, Laurie Berrie, Matthew P Fox, Sarah C Gadd, Wendy J Harrison, Claire Keeble, Lynsie R Ranker, Johannes Textor, Georgia D Tomova, Mark S Gilthorpe, and George T H Ellison. 2020. "Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations," International Journal of Epidemiology. dyaa213 [OUP]
Randomized Controlled Trials
- Alan S. Gerber and Donald P. Green. 2012. Field Experiments: Design, Analysis, and Interpretation. W W Norton & Co Inc [Amazon]
Field Experiments
Laboratory Experiments
Survey Experiments
- Song Jaehyun・秦正樹. 2020. 「オンライン・サーベイ実験の方法: 理論編」『理論と方法』35 (1): 92-108.
- 秦正樹・Song Jaehyun. 2020. 「オンライン・サーベイ実験の方法: 実践編」『理論と方法』35 (1): 109-127.
Survey Experiments: Priming/Framing
Survey Experiments: List
- (多変量解析を用いた被験者の属性ごとの予測値の推定1) Imai Kosuke. 2011. "Multivariate Regression Analysis for the Item Count Technique." Journal of the American Statistical Association. 106(494):407–416.
- (多変量解析を用いた被験者の属性ごとの予測値の推定2) Blair Graeme, Imai Kosuke. 2012. "Statistical Analysis of List Experiments." Political Analysis. 20(1):47–77
- (Cobminedリスト実験とその仮定の検定) Peter M. Aronow, Alexander Coppock, Forrest W. Crawford, and Donald P. Green. 2015. "Combining List Experiment and Direct Question Estimates of Sensitive Behavior Prevalence." Journal of Survey Statistics and Methodology, 3(1):43–66. [Oxford]
- (予測値を説明変数として用いる手法) Kosuke Imai, Bethany Park, and Kenneth F. Greene. 2017. "Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models." Political Analysis. 23(2): 180-196. [Cambridge]
- Winston Chou, Kosuke Imai, and Bryn Rosenfeld. 2020. "Sensitive Survey Questions with Auxiliary Information." Sociological Methods & Research. 49(2): 418-454. [Sage]
- (洗練化されたリスト実験) Tsuchiya Takahiro, Hirai Yoko. 2010. "Elaborate Item Count Questioning: Why Do People Underreport in Item Count Responses?" Survey Research Methods. 4(3):139–149. [OJS]
Survey Experiments: Conjoint
- (解説1) Jens Hainmueller, Daniel J. Hopkins, and Teppei Yamamoto. 2014. "Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments." Political Analysis. 22(1):1–30. [Cambridge]
- (解説2) Kirk Bansak, Jens Hainmueller, Dan Hopkins, and Teppei Yamamoto. "Conjoint Survey Experiments," in Jamie Druckman and Don Green ed. Cambridge Handbook of Advances in Experimental Political Science. [Paper]
- (AMCEについて) Kirk Bansak, Jens Hainmueller, Daniel J. Hopkins, and Teppei Yamamoto. "Using Conjoint Experiments to Analyze Elections: The Essential Role of the Average Marginal Component Effect (AMCE)" SSRN. [SSRN]
- (MMについて) Thomas J. Leeper, Sara B. Hobolt, and James Tilley. 2019. "Measuring Subgroup Preferences in Conjoint Experiments," Political Analysis. 28(2): 207-221. [Cambridge]
- (属性数について) Kirk Bansak, Jens Hainmueller, Daniel J. Hopkins, and Teppei Yamamoto. 2019. "Beyond the Breaking Point? Survey Satisficing in Conjoint Experiments," Political Science Research and Methods. [Cambridge]
- (タスク数について) Kirk Bansak, Jens Hainmueller, Daniel J. Hopkins, and Teppei Yamamoto. 2018. "The Number of Choice Tasks and Survey Satisficing in Conjoint Experiments." Political Analysis. 26 (1): 112-119. [Cambridge]
- (日本の事例) 宋財泫・善教将大. 2016. 「コンジョイント実験の方法論的検討」『法と政治』67(2): 67-108. [KGUR]
- (外的妥当性について1) Jens Hainmueller, Dominik Hangartner, and Teppei Yamamoto. 2015. "Validating vignette and conjoint survey experiments against real-world behavior." Proceedings of the National Academy of Sciences of the United States of America, 112(8), 2395–2400. [PNAS]
- (外的妥当性について2) Brandon de la Cuesta, Naoki Egami, and Kosuke Imai. 2021. "Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution." Political Analysis. [Cambridge]
Matching
Difference in Difference
- Difference-in-Difference [Web]
- Coady Wing, Kosali Simon, and Ricardo A. Bello-Gomez. 2018. "Designing Difference in Difference Studies: Best Practices for Public Health Policy Research." Annual Review of Public Health. 39:453-469. [Annual Review]
Synthetic Control Method
- 前田豊・鎌田 拓馬. 2019.「Synthetic Control Methodを用いた個別事例の因果効果の識別」『理論と方法』34 (1): 78-96 [J-STAGE]
Regression Discontinuity Design
- Matias D. Cattaneo, Nicolás Idrobo, and Rocío Titiunik. 2020. A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press. [Amazon] [arXiv]
Instrumental Variable
バランスチェック
- Stefan Tübbicke. "Entropy Balancing for Continuous Treatments." arXiv:2001.06281 [arXiv]
計量経済学
- Bruce E. Hansen. Econometrics. [Web]