http://causalinference.gitlab.io/kdd-tutorial
Books
- Pearl, Judea. Causality. Cambridge university press, 2009.
- Morgan, Stephen L., and Christopher Winship. Counterfactuals and causal inference. Cambridge University Press, 2015.
- Imbens, Guido W., and Donald B. Rubin. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press, 2015.
- Dunning, Thad. Natural experiments in the social sciences: a design-based approach. Cambridge University Press, 2012.
- Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. MIT Press, 2017.
Papers
- Bottou, Léon, et al. “Counterfactual reasoning and learning systems: The example of computational advertising.” The Journal of Machine Learning Research 14.1 (2013): 3207-3260.
- Eckles, Dean, and Eytan Bakshy. “Bias and high-dimensional adjustment in observational studies of peer effects.” arXiv preprint arXiv:1706.04692 (2017).
- Olteanu, Alexandra, Onur Varol, and Emre Kiciman. “Distilling the outcomes of personal experiences: A propensity-scored analysis of social media.” Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, 2017.
- Shalizi, Cosma Rohilla, and Andrew C. Thomas. “Homophily and contagion are generically confounded in observational social network studies.” Sociological methods & research 40.2 (2011): 211-239.
- Sharma, Amit, Jake M. Hofman, and Duncan J. Watts. “Split-door criterion for causal identification: Automatic search for natural experiments.” arXiv preprint arXiv:1611.09414 (2016).
- Wager, Stefan, and Susan Athey. “Estimation and inference of heterogeneous treatment effects using random forests.” Journal of the American Statistical Association (2017).
Workshop2: The 2018 ACM SIGKDD Workshop on Causal Discovery
http://nugget.unisa.edu.au/CD2018/
http://nugget.unisa.edu.au/CD2018/program.html
最后更新: 2018年09月03日 09:54