This paper deals with the problem of estimating the probability that one event was a cause of another in a given scenario. Using structural-semantical definitions of the probabilities of necessary or sufficient causation (or both), we show how to optimally bound these quantities from data obtained in experimental and observational studies, making minimal assumptions concerning the data-generating process. In particular, we strengthen the results of Pearl (1999) by weakening the data-generation assumptions and deriving theoretically sharp bounds on the probabilities of causation. These results delineate precisely how empirical data can be used both in settling questions of attribution and in solving attribution-related problems of decision making.
General sample size analysis for probabilities of causation: a delta method approach
Department of Statistics, Florida State University（佛罗里达州立大学统计学系）；Department of Computer Science, Cornell University（康奈尔大学计算机科学系）；Department of Computer Science, University of California Los Angeles（美国加州大学洛杉矶分校计算机科学系）；Department of Computer Science, Florida State University（佛罗里达州立大学计算机科学系）
Partial Identification under Causal Orders by Linear Programming
Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)（达勒·莫勒人工智能研究所）；Scuola Universitaria Professionale della Svizzera Italiana (SUPSI)（瑞士意大利语区应用科技大学）
National Research Institute for Mathematics and Computer Science in the Netherlands (CWI)（荷兰国家数学与计算机科学研究院）；University of Amsterdam（阿姆斯特丹大学）；Warwick Business School, University of Warwick（华威大学华威商学院）
Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
Toyota Research Institute of North America（丰田美国研究院）；Toyota Motor North America Frontier Research Center（丰田北美前沿研究中心）；Toyota Motor Corporation（丰田公司）；Toyota Motor North America（丰田北美）
