基本信息
浏览量:199
职业迁徙
个人简介
Much of epidemiology, econometrics, sociology and political science is an attempt to infer causal relationships using data gathered under conditions where fully controlled experiments are not possible. The goals of my current research (called the TETRAD project) can be divided into two main parts. The first goal is to specify and prove under what conditions it is possible to reliably infer causal relationships from background knowledge and statistical data not obtained under fully controlled conditions. The second goal is to develop, analyze, implement, test and apply practical, provably correct computer programs for inferring causal structure under conditions where this is possible. The results of this research are available in the TETRAD II computer program.
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.
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.
研究兴趣
论文共 230 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
ICLR 2024 (2024)
引用0浏览0引用
0
0
arxiv(2024)
引用0浏览0引用
0
0
ICLR 2024 (2023)
引用0浏览0EI引用
0
0
Xinshuai Dong,Biwei Huang,Ignavier Ng,Xiangchen Song,Yujia Zheng, Songyao Jin, Roberto Legaspi,Peter Spirtes,Kun Zhang
CoRR (2023)
引用0浏览0EI引用
0
0
Negar Kiyavash,Elias Bareinboim, Todd Coleman, Alex Dimakis, Bernhard Schlkopf,Peter Spirtes,Kun Zhang,Robert Nowak
IEEE Journal on Selected Areas in Information Theory (2023): iv-iv
American journal of epidemiologyno. 11 (2023): 1917-1927
Conference on Causal Learning and Reasoning (CLeaR) (2022): 861-876
引用0浏览0EI引用
0
0
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn