Peter Spirtes

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个人简介

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Peter Spirtes是卡内基梅隆大学的Marianna Brown Dietrich教授和哲学系主任。他的研究兴趣跨越多个基础学科,涉及哲学、统计学、图论和计算机科学。他的研究对一些需要从统计数据中进行因果推断的学科有着深刻的影响。

Peter Spirtes与Clark Glymour一起提出了最早的因果发现算法之一,即PC。他还发表了该领域广泛使用的参考书之一("Causation, Prediction, and Search[1]")。他的工作表明,在某些情况下,有一些计算机程序可以在合理的假设下从实验或非实验数据,或两者的组合中,可靠地得出有用的因果结论。他目前的研究集中如何将因果科学的基础假设弱化,从而将结果的应用扩展到更广泛,更通用的场景中。同时,他也关注因果发现在大规模数据上的引用。

Peter Spirtes的研究对包括生物学在内的许多不同学科都有重要的理论和实践意义。在理论上,它帮助我们理解了概率和因果关系之间的关系,以及在各种不同的假设下,从各种数据中进行可靠的因果推断的确切限度是什么。在实践上,它为科学家提供了一个有用的工具,帮助他们建立因果模型。

代表工作

Spirtes, P., Zhang, J. (forthcoming) “A Uniformly Consistent Estimator of Causal Effects Under The k-Triangle-Faithfulness Assumption”, Statistical Science.


Spirtes, P., (2013) "Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models", Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13), AUAI Press, 2013, pp. 606-615.


Ramsey, J., Spirtes, P, and Glymour, C. (2011) “On meta-analyses of imaging data and the mixture of records.” NeuroImage 57(2): 323-330.


Zhang, J., and Spirtes, P. (2011) "Intervention, Determinism, and the Causal Minimality Condition”, Synthese, 2011, 182:13, pp. 335-347.


Ali, A., Richardson, T., Spirtes, P. (2009) “Markov Equivalence For Ancestral Graphs”, Annals of Statistics, 37, 5B, 2808-2837.


Tillman, R., Gretton, A. and Spirtes, P. (2009) “Nonlinear directed acyclic structure learning with weakly additive noise models”, NIPS 2009.


Spirtes, P. (2009) "Variable Definition and Causal Inference", Proceedings of the 13th International Congress of Logic Methodology and Philosophy of Science, pp. 514-53.


Zhang, J., and Spirtes, P. (2009) "Detection of Unfaithfulness and Robust Causal Inference", Minds and Machines, 18:2, pp. 239-272.


Silva, R., Glymour, C., Scheines, R. and Spirtes, P. (2006) “Learning the Structure of Latent Linear Structure Models,” Journal of Machine Learning Research, 7, 191-246.


Ramsey, J., Zhang, J., and Spirtes, P., (2006) “Adjacency-Faithfulness and Conservative Causal Inference”, Uncertainty in Artificial Intelligence 2006, Boston, MA.


Spirtes, P. (2005) “Graphical Models, Causal Inference, and Econometric Models”, Journal of Economic Methodology. 2005 12:1,  pp. 1–33.


Zhang, J., and Spirtes, P. (2005) “A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables”, Uncertainty in Artificial Intelligence 2005, Edinboro, Scotland.


Ali, R., Richardson, T.,  Spirtes, P., and Zhang, J. (2005) “Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graph Models with Latent Variables”, Uncertainty in Artificial Intelligence 2005, Edinboro, Scotland.


Spirtes, P., and Scheines, R. (2004).  “Causal Inference of Ambiguous Manipulations”, in Proceedings of the Philosophy of Science Association Meetings, 2002.


Chu, T., Glymour, C., Scheines, R., Spirtes, P. (2003) “A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays”, Bioinformatics, 19, pp. 1147-1152.  


Robins, J., Scheines, R., Spirtes, P., and Wasserman, L. (2003). “Uniform Consistency in Causal Inference”, Biometrika, September,  90: pp. 491 – 515.


Zhang, J., and Spirtes, P. (2003) “Strong Faithfulness and Uniform Consistency in Causal Inference”, UAI '03, Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence, August 7-10 2003, Acapulco, Mexico, ed. by Christopher Meek and Uffe Kjarulff, Morgan Kaufmann.


Richardson, T., Spirtes, P. (2002) “Ancestral Graph Markov Models”, Annals of Statistics, 2002, 30 pp. 962-1030.


Spirtes, P., Glymour, C. and Scheines, R. (2000). Causation, Prediction, and Search, 2nd ed. New York, N.Y.: MIT Press.


Spirtes, P., Glymour, C., and Scheines, R. (2000) Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data, to appear in the Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems & Technology.


Robins, J., Scheines, R., Spirtes, P., and Wasserman, L. (2000) Uniform Consistency in Causal Inference, Carnegie Mellon University Department of Statistics Technical Report 725.


Richardson, T., and Spirtes, P. (2000) Ancestral Markov Graphical Models, University of Washington Department of Statistics Technical Report 375.


Spirtes, P. (2000) An Anytime Algorithm for Causal Inference, to be presented at AI and Statistics 2001.


Spirtes, P. (1997). Limits on Causal Inference from Statistical Data, presented at American Economics Association Meeting.


Spirtes, P., Cooper, G. (1997). An Experiment in Causal Discovery Using a Pneumonia Database, Proceedings of AI and Statistics 99.


Spirtes, P., Richardson, T., Meek, C. (1997). The Dimensionality of Mixed Ancestral Graphs, Technical Report CMU-83-Phil.


Spirtes, P., Richardson, T., Meek, C., Scheines, R., and Glymour, C. (1997). Using Path Diagrams as a Structural Equation Modelling Tool, Technical Report CMU-82-Phil.


Scheines, R., Spirtes, P., Glymour, C., Meek, C., and Richardson, T. (forthcoming). The TETRAD Project: Constraint Based Aids to Causal Model Specification, Multivariate Behavioral Research


Spirtes, P., Glymour, C. and Scheines, R. (1993). Causation, Prediction, and Search, New York, N.Y.: Springer-Verlag.


Scheines, R. (forthcoming). An Introduction to Causal Inference, in Causality in Crisis, ed. by Steven Turner and Vaughan McKim, University of Notre Dame Press.


Spirtes, P., Richardson, T., Meek, C., Scheines, R., and Glymour, C., (1996). Using D-separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors, Technical Report CMU-72-Phil.


Spirtes, P., and Richardson, T. (1996). A Polynomial Time Algorithm For Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias, Proceedings of the 6th International Workshop on Artificial Intelligence and Statistics.


Spirtes, P., Richardson, T., and Meek, C. (1996). Heuristic Greedy Search Algorithms for Latent Variable Models, Proceedings of the 6th International Workshop on Artificial Intelligence and Statistics.


Richardson, T., and Spirtes, P. (1996). Automated discovery of linear feedback models, Technical Report CMU-75-Phil.


Spirtes, P., and Scheines, R. (forthcoming). Reply to Freedman, in Causality in Crisis, ed. by Steven Turner and Vaughan McKim, University of Notre Dame Press.


Spirtes, P., Meek, C., and Richardson, T. (1996). Causal Inference in the Presence of Latent Variables and Selection Bias, Technical Report CMU-77-Phil.


Spirtes, P. (1995). Directed Cyclic Graphical Representation of Feedback Models, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, ed. by Philippe Besnard and Steve Hanks, Morgan Kaufmann Publishers, Inc., San Mateo, 1995.

编者推荐

集智学园课程推荐

图灵奖得主朱迪亚·珀尔教授认为,当下正在进行一场改变数据科学的新革命 ”因果革命“。它以科学为中心,涉及从数据到政策、可解释性、机制的泛化,再到一些社会科学中的归因和公平性问题,甚至哲学中的创造性和自由意志 。本季读书会以Elements of Causal Inference一书为线索,主要展现因果科学在机器学习各个方向上的影响,包括强化学习、迁移学习、表示学习等等,并分享在工业界的部分应用成果。本季读书会梳理了因果科学的核心内容,理解它如何改变数据科学,助力 AI 系统超越曲线拟合和获得回答因果问题的能力。

因果推断与机器学习领域的结合已经吸引了越来越多来自学界业界的关注。第一季读书会主要关注了因果科学在机器学习方向上的前沿应用,为深入探讨、普及推广因果科学议题,第二季读书会着力于实操性、基础性,带领大家精读因果科学方向两本非常受广泛认可的入门教材:Causal inference in statistics: A primer和Elements of causal inference: foundations and learning algorithms。读书会以直播讨论为主,结合习题交流、夜谈、编程实践、前沿讲座等多类型内容,主要面向有机器学习背景、希望深入学习因果科学基础知识和重要模型方法、寻求解决相关研究问题的研究人员。

“因果”并不是一个新概念,而是一个已经在多个学科中使用了数十年的分析技术。通过前两季的分享,我们主要梳理了因果科学在计算机领域的前沿进展。如要融会贯通,我们需要回顾数十年来在社会学、经济学、医学、生物学等多个领域中,都是使用了什么样的因果模型、以什么样的范式、解决了什么样的问题。我们还要尝试进行对比和创新,看能否以现在的眼光,用其他的模型,为这些研究提供新的解决思路。

“因果+X”就是要让因果真正地应用于我们的科学研究中,不管你是来自计算机、数理统计领域,还是社会学、经济学、管理学领域,还是医学、生物学领域,我们希望共同探究出因果研究的范式,真正解决因果的多学科应用问题,乃至解决工业界的问题。

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相关路径

  1. Spirtes, Peter, et al. Causation, prediction, and search. MIT press, 2000.