Paper&People
从论文的角度来介绍因果推断领域的研究者。
Causal and Counterfactual Inference 因果推断框架一个简要的数学介绍。
Causal and Counterfactual Inference 过于重要，需要背诵论文中所有的内容。
Judea Pearl 有三篇必读论文
J. Pearl, "The Seven Tools of Causal Inference, with Reflections on Machine Learning," July 2018. Communications of ACM, 62(3): 54-60, March 2019
J. Pearl, "Causal and Counterfactual Inference ," October 2019. Forthcoming section in The Handbook of Rationality, MIT Press.
J. Pearl, "Causal inference in statistics: An overview," Statistics Surveys, 3:96--146, 2009.
和两本必读书籍
The Book of Why: The New Science of Cause and Effect (with Dana Mackenzie), New York: Basic Books, May 2018
Causality: Models, Reasoning, and Inference, Cambridge University Press, 2000; 2nd edition, 2009.
其学生 Elias Bareinboim, the director of the Causal Artificial Intelligence Lab at Columbia University 的关于 Causal AI 综述论文：
Causal Inference and Data-Fusion in Econometrics
其团队的"Foundations and new horizons for causal inference"研讨会特别好！The talks and discussions at the workshop will help to shape the field in the coming years.
潜结果框架下的因果研究综述
本文给出了潜结果框架清晰准确的描述，按照其假设全面的梳理了该框架下的因果效应估计方法。
该综述是一个综述文章的标杆，里面推荐了如下几个人的研究工作， see more on colab。
David Lopez-Paz, a research scientist at Facebook AI Research, leads very interesting research on casual inference in general, and in the context of learning frameworks and deep learning specifically. Highlights include posing causal inference as a learning problem (specifically of classifying probability distributions), causal generative neural networks, incorporation of an adversarial framework for causal discovery and discovering causal signals in images.
Krzysztof Chalupka has done some fascinating research in the intersection of deep learning and causal inference. Highlights include a deep-learning-based conditional independence test, causal feature learning, visual causal feature learning and causal regularization.
Finally, [Dong et al. 2012] have used Multi-Step Granger Causality Method (MSGCM), a method developed to identify feedback loops embedded in biological networks using time-series experimental measurements, for the identification of feedback loops in neural networks.
