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形式初稿
== 基本信息 ==
== 基本信息 ==
[[文件:Judea Pearl.png|替代=朱迪亚·珀尔|缩略图|朱迪亚·珀尔|边框|左|483x483像素]]
[[文件:Judea Pearl.png|替代=朱迪亚·珀尔|缩略图|朱迪亚·珀尔|边框|左|566x566px]]
{| class="wikitable"
{| class="wikitable"
|+
|+
|著名成就
|著名成就
|贝叶斯网络
|贝叶斯网络
do算子（do-calculus）
珀尔因果层次模型（PCH: Pearl Causal Hierarchy）
|-
|-
|主要研究方向
|主要研究方向
|人工智能、因果推理和科学哲学
|人工智能
因果推理
科学哲学
|-
|-
|教育院校
|教育院校
== 研究领域 ==
== 研究领域 ==
节选自[https://amturing.acm.org/award_winners/pearl_2658896.cfm AMC图灵奖人物主页]
=== 搜索和启发式 ===
=== 搜索和启发式 ===
Pearl 在计算机科学领域的声誉最初不是建立在概率推理（这在当时是一个有争议的话题）上，而是建立在组合搜索上。从1980年开始发表一系列期刊论文，最终于1984年 Pearl 出版了《启发式：计算机问题解决的智能搜索策略》一书。这项工作包括许多关于传统搜索算法的新结果，例如A*算法（发音：A star algorithm），以及在游戏算法方面，将人工智能研究提升到一个新的严谨和深度水平。它还提出了关于如何从宽松的问题定义中自动推导出可接受的启发式的新想法，这种方法导致了规划系统的巨大进步。
Pearl 在计算机科学领域的声誉最初不是建立在概率推理（这在当时是一个有争议的话题）上，而是建立在组合搜索上。从1980年开始发表一系列期刊论文，最终于1984年 Pearl 出版了《启发式：计算机问题解决的智能搜索策略》一书。这项工作包括许多关于传统搜索算法的新结果，例如A*算法（发音：A star algorithm），以及在游戏算法方面，将人工智能研究提升到一个新的严谨和深度水平。它还提出了关于如何从宽松的问题定义中自动推导出可接受的启发式的新想法，这种方法导致了规划系统的巨大进步。
=== 因果关系 ===
=== 因果关系 ===
即使在开发贝叶斯概率网络的理论和技术时，Pearl 也怀疑需要一种不同的方法来解决他多年来一直关注的因果关系问题。在他2000年关于因果关系的著作《因果关系：模型、论证、推理》中，他描述了他早期的兴趣如下：<blockquote>在我高中三年级的时候，我第一次看到了因果关系的黑暗世界。我的科学老师 Feuchtwanger 博士通过讨论19世纪的发现，向我们介绍了逻辑研究，发现死于天花接种的人比死于天花本身的人多。一些人利用这些信息争辩说接种是有害的，而事实上，数据证明恰恰相反，接种通过根除天花来挽救生命。
Feuchtwanger 博士总结道：“逻辑的用武之地就是保护我们免受此类因果谬误的影响。” 当时的我们都为逻辑的奇迹而折服，尽管 Feuchtwanger 博士从未真正向我们展示过逻辑如何保护我们免受这些谬误的影响。
然而多年后我作为一名人工智能研究员意识到，事实并非如此。逻辑学和数学的任何分支都没有开发出足够的工具来管理类似天花疫苗这样涉及因果关系的问题。</blockquote>实际上，贝叶斯网络无法捕获因果信息，例如“吸烟”-->“肺癌”，它在数学上等同于网络“肺癌”-->“吸烟”。因果网络的关键特征是它能捕捉外生干预变量的潜在效果。在因果网络X-->Y中，人为设定Y的值对Y实施干预，不应该改变人对X的先验认知，即，对Y的干预切断了从X到Y的影响链；因此，因果网络“吸烟”-->“肺癌”能够反映我们关于真实世界如何运作的信念（迫使受试者吸烟确实能改变一个人的信念，使他相信这会让受试者会患上癌症），而“癌症”-->“吸烟”则不能反映我们对真实世界的理解（如果受试者因为人为诱导而患上癌症，则不会改变一个人对该受试者是否吸烟的信念）。这个 Pearl 称之为do-calculus的简单分析，导致了一套完整数学框架的出现，对因果模型做了形式化，并能通过分析数据确定因果关系。这项工作推翻了长期以来人们对统计学的看法，即因果关系只能通过受控随机试验来确定——多数情况下，在生物和社会科学等领域实施随机受控实验是不可能的。
== 奖项与成就 ==
== 奖项与成就 ==
2011 年，他获得了ACM的图灵奖，这是计算机工程领域的最高荣誉，以表彰他“通过开发用于概率和因果推理的微积分对人工智能的根本贡献”。
2011 年，他获得了ACM的图灵奖，这是计算机工程领域的最高荣誉，以表彰他“通过开发用于概率和因果推理的微积分对人工智能的根本贡献”。
2015年，ACM fellow
== 主要文章及著作 ==
== 主要文章及著作 ==
2018 年，《为什么：关于因果关系的新科学》 The Book of Why: The New Science of Cause and Effect (with Dana Mackenzie), New York: Basic Books, May 2018
2018 年，《为什么：关于因果关系的新科学》 The Book of Why: The New Science of Cause and Effect (with Dana Mackenzie), New York: Basic Books, May 2018
== 个人生活 ==
== 相关链接 ==
* [http://bayes.cs.ucla.edu/jp_home.html 个人主页]
* [https://amturing.acm.org/award_winners/pearl_2658896.cfm AMC图灵奖人物主页]
* [https://scholar.google.com/citations?hl=en&user=bAipNH8AAAAJ 谷歌学术个人主页]
* [[wikipedia:Judea_Pearl|维基主页]]
== 研究领域 ==
== 参考文献 ==
# Pearl, J., “Asymptotic properties of minimax trees and game-searching procedures,” ''Artificial Intelligence'', 14, pp. 113–138, September 1980. ''One of the first papers to establish “phase transition” properties for a combinatorial problem; introduced new mathematical techniques into the AI literature''.
# <span name="bib_2"></span>Pearl, J., “Knowledge versus search: A quantitative analysis using A*,” ''Artificial Intelligence'', Vol. 20, pp. 1–13, 1983. ''Proved the first results relating heuristic accuracy to search algorithm complexity.''
J. Pearl, "Theoretical Impediments to Machine Learning with Seven Sparks from the Causal Revolution"
# <span name="bib_3"></span>Pearl, J., “On the nature of pathology in game searching,” ''Artificial Intelligence'', Vol. 20, pp. 427–453, 1983. ''Proved that, under the standard model of game trees, deeper search does not necessarily improve play; and showed that this paradox is resolved by correct probabilistic updating of beliefs.''
# <span name="bib_4"></span>Karp, R. and J. Pearl, “Searching for an optimal path in a tree with random costs," ''Artificial Intelligence'', Vol. 21, pp. 99–116, 1983. ''Identified a phase transition property for a very simple path-finding problem, with significant complexity implications.''
# <span name="bib_5"></span>Pearl, J., “On the discovery and generation of certain heuristics,” ''AI Magazine'', Winter/Spring, pp. 23–33, 1983. ''The first paper on the systematic generation of admissible heuristics (lower bounds on optimal solution costs) by relaxing formally represented problem definitions; this idea led to dramatic advances in automated planning systems.''
# <span name="bib_6"></span>Pearl, J., ''Heuristics'': ''Intelligent Search Strategies for Computer Problem Solving,'' Addison-Wesley, 1984. ''Synthesized essentially everything known up to that point about intelligent methods for search and game playing, much of it Pearl’s own work; also the first textbook to treat AI topics formally at a technically advanced level.''
# <span name="bib_7"></span>Dechter, R. and J. Pearl, “Generalized best-first search strategies and the optimality of A*,” ''Journal of the Association for Computing Machinery'', Vol. 32, pp. 505–536, 1985. Available here.''Proved that A* is the most efficient member of a very broad class of problem-solving algorithms.''
# <span name="bib_7"></span>Pearl, J., “Reverend Bayes on inference engines: A distributed hierarchical approach,” ''Proceedings, AAAI-82'', 1982. ''The paper that began the probabilistic revolution in AI by showing how several desirable properties of reasoning systems can be obtained through sound probabilistic inference. It introduced tree-structured networks as concise representations of complex probability models, identified conditional independence relationships as the key organizing principle for uncertain knowledge, and described an efficient, distributed, exact inference algorithm.''
# Kim, J. and J. Pearl, “A computational model for combined causal and diagnostic reasoning in inference systems,” ''Proceedings, IJCAI-83'', 1983. ''Generalized the tree-structured network to allow for multiple parents, or causal influences, on any given variable.''
# <span name="bib_10"></span>Pearl, J., “Learning hidden causes from empirical data,” ''Proceedings, IJCAI-85'', 1985. ''Initiated the study of methods for learning the structure of probabilistic causal models.''
J. Pearl, "Comments on `The Tale Wagged by the DAG'"
# <span name="bib_11"></span>Pearl, J., “On the logic of probabilistic dependencies,” ''Proceedings, AAAI-86'', 1986. ''One of several papers establishing the connection between graphical models and conditional independence relationships.''
# <span name="bib_12"></span>Pearl, J., “Fusion, propagation and structuring in belief networks,” ''Artificial Intelligence'', Vol. 29, pp. 241–288, 1986. ''The key technical paper on representation and exact inference in general Bayesian networks; by 1991 this had become the most cited paper in the history of the Artificial Intelligence journal.''
# <span name="bib_13"></span>Pearl J. and A. Paz, “Graphoids: A graph-based logic for reasoning about relevance relations,” In B. du Boulay et al. (Eds.), ''Advances in Artificial Intelligence II'', North-Holland, 1987. ''Establishes an axiomatic characterization of the properties that enable probabilities and other relational systems to be represented by graphs.''
# <span name="bib_14"></span>Pearl, J., “Evidential reasoning using stochastic simulation of causal models,” ''Artificial Intelligence'', Vol. 32, pp. 245–258, 1987. ''Derived a general approximation algorithm for Bayesian network inference using Markov chain Monte Carlo (MCMC); this was the first significant use of MCMC in mainstream AI.''
# <span name="bib_15"></span>Pearl, J., ''Probabilistic Reasoning in Intelligent Systems'', Morgan Kaufmann, 1988. ''Explained the philosophical, cognitive, and technical basis for a probabilistic view of knowledge, reasoning, and decision making. One of the most cited works in the history of computer science, this book initiated the modern era in AI and converted many researchers who had previously worked in the logical and neural-network communities.''
# <span name="bib_15"></span> Pearl J. and T.S. Verma, “A theory of inferred causation,” ''Proceedings, KR-91'', 1991. ''Introduces minimal-model semantics as a basis for causal discovery, and shows that causal directionality can be inferred from patterns of correlations without resorting to temporal information.''
# Pearl, J., “Graphical models, causality, and intervention,” ''Statistical Science'', Vol. 8, pp. 266–269, 1993. ''Introduces the back-door criterion for covariate selection, the first to guarantee bias-free estimation of causal effects.''
# Pearl, J., “Causal diagrams for empirical research,” ''Biometrika'', Vol. 82, Num. 4, pp. 669–710, 1995. ''Introduces the theory of causal diagrams and its associated do-calculus; the first (and still the only) mathematical method to enable a systematic removal of confounding bias in observations.''
# Pearl, J., “The Art and Science of Cause and Effect,” ''UCLA Cognitive Systems Laboratory, Technical Report R-248'', 1996. ''Transcript of lecture given Thursday, October 29, 1996, as part of the UCLA 81st Faculty Research Lecture Series.Used later as epilogue to the book Causality (2000). Provides a panoramic view of the historical development of causal thoughts from antiquity to modern days.''
# Pearl, J., ''Causality'': ''Models, Reasoning, and Inference,'' Cambridge University Press, 2000. ''Building on theoretical results from 1987 to 2000, lays out a complete framework for causal discovery, interventional analysis and counterfactual reasoning, bringing mathematical rigor and conceptual clarity to an area previously considered off-limits for statistics. Winner of the 2001 Lakatos Prize for the most significant new work in the philosophy of science.''
# Pearl, J., “The logic of counterfactuals in causal inference (Discussion of `Causal inference without counterfactuals' by A.P. Dawid),” ''Journal of American Statistical Association'', Vol. 95, pp. 428–435, 2000. ''Demonstrates how counterfactual reasoning underlines scientific thought and argues against its exclusion from statistical analysis.''
# Tian, J. and J. Pearl, “Probabilities of causation: Bounds and identification,” ''Annals of Mathematics and Artificial Intelligence'', Vol. 28, pp. 287–313, 2000. ''Derives tight bounds on the probability that one observed event was the cause of another, in the legal sense of "but for," thus providing a principled way of substantiating guilt and innocence from data.''
# Pearl, J., “Robustness of causal claims,” ''Proceedings, UAI-04'', 2004. ''Offers a formal definition of robustness and develops a method for assessing the degree to which causal claims are robust to model misspecification.''
# Pearl, J., “Direct and indirect effects,” ''Proceedings, UAI-01'', 2001. ''Establishes the theoretical basis of modern mediation analysis. Derives the "Mediation Formula" and provides graphical conditions for the identification of direct and indirect effect.''
# Tian, J. and J. Pearl, “A general identification condition for causal effects,” ''Proceedings, AAAI-02'', 2002. ''Uses the do-calculus to derive a general graphical condition for identifying causal effects from a combination of data and assumptions.''
# Halpern, J. and J. Pearl, “Causes and explanations: A structural-model approach—Parts I and II,” ''British Journal for the Philosophy of Science'', Vol. 56, pp. 843–887 and 889–911, 2005. ''Establishes counterfactual conditions for one event to be perceived as the “actual cause” of another and for one event to provide an “explanation” of another.''
# Pearl, J., “Causal inference in statistics: An overview,” ''Statistics Surveys'', Vol. 3, pp. 96–146, 2009. ''Describes a unified methodology for causal inference based on a symbiosis between graphs and counterfactual logic.''
# Pearl, J., “The algorithmization of counterfactuals,” ''Annals of Mathematics and Artificial Intelligence'', Vol. 61, pp. 29–39, 2011. ''Describes a computational model that explains how humans generate, evaluate and distinguish counterfactual statements so swiftly and consistently.''
# Pearl J. and E. Bareinboim, “Transportability of causal and statistical relations: A formal approach,” ''Proceedings, AAAI-11'', 2011. ''Reduces the classical problem of external validity to mathematical transformations in the do-calculus, and establishes conditions under which experimental results can be generalized to new environments in which only passive observation can be conducted.''
Journal of Causal Inference, Causal, Casual, and Curious Section, 6(1), Article 20180005, March 2018. <nowiki>https://doi.org/10.1515/jci-2018-0005</nowiki>
A. Forney, J. Pearl, and E. Bareinboim, "Counterfactual Data-Fusion for Online Reinforcement Learners"
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1156-1164, 2017.
J. Pearl, "The Eight Pillars of Causal Wisdom"
(R-469):
J. Pearl, "A Personal Journey into Bayesian Networks,"
(R-466): [pdf] [bib]
J. Pearl "The Sure-Thing Principle"
UCLA Cognitive Systems Laboratory, Technical Report (R-461-L), April 2016.
(R-460): [pdf] [bib]
J. Pearl, "A Linear `Microscope' for Interventions and Counterfactuals"
Journal of Causal Inference, Causal, Casual, and Curious Section, published online 5(1):1-15, March 2017.
'''[1]J. Pearl''', "A Personal Journey into Bayesian Networks,"
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