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个人简介
My research is interdisciplinary in nature, involving philosophy, statistics, graph theory and computer science. It has implications for the practices of a number of disciplines in which causal inferences from statistical data are made. The research that I have described shows that there are computer programs which can sometimes reliably draw useful causal conclusions under a reasonable set of assumptions. But there are still many cases where the assumptions I have made are known to be false. My current research centers on the extent to which these limiting assumptions can be relaxed, thereby extending the application of the results to a much wider class of phenomena, and investigating the extent to which these search procedures can be made more reliable on small samples. This research program has important theoretical and practical implications. Theoretically, it will help us understand the relationship between probability and causality, and what the precise limits of reliable inference from uncontrolled data are. Practically, it will provide a useful tool for scientists that will help them build causal models.
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IEEE Trans. Neural Networks Learn. Syst.no. 4 (2024): 4899-4901
American journal of epidemiology (2024)
ICLR 2024 (2024)
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Xinshuai Dong,Biwei Huang,Ignavier Ng,Xiangchen Song,Yujia Zheng, Songyao Jin, Roberto Legaspi,Peter Spirtes,Kun Zhang
CoRR (2023)
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Negar Kiyavash,Elias Bareinboim, Todd P. Coleman, Alex Dimakis, Bernhard Schlkopf,Peter Spirtes,Kun Zhang ,Robert Nowak
IEEE J. Sel. Areas Inf. Theory (2023): iv-iv
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