“Judea Pearl”的版本间的差异

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教授，工作于计算机科学系  加州大学洛杉矶分校
 
教授，工作于计算机科学系  加州大学洛杉矶分校
  
−
获得图灵奖在2012年，因为发明贝叶斯网络
+
电话：（310）825-3243
 +
 
 +
传真：（310）825-2273
 +
 
 +
电子邮件：''judea@cs.ucla.edu''
  
 
[http://bayes.cs.ucla.edu/jp_home.html J. Pearl 的网页]
 
[http://bayes.cs.ucla.edu/jp_home.html J. Pearl 的网页]
 +
 +
朱迪亚·珀尔（Judea Pearl）——以色列裔美籍计算机科学家、哲学家，以倡导人工智能的概率方法和贝叶斯网络而闻名。他还因在结构模型的基础上发展出因果和反事实推论而受到广泛称赞。2011年，ACM授予Judea Pearl图灵奖，以表彰他“通过发展概率和因果推理演算对人工智能做出的基础性贡献”。他早在40多年前便通过贝叶斯网络的设计，使机器实现概率推理而在人工智能领域声名大噪，并被誉为“贝叶斯网络之父”，但近年却公开声称自己其实是人工智能社区的一名“叛徒”：离开了主流追逐、并且也是由他奠定重要理论基础和方法论的概率推理，而去追求一项更具挑战性的任务——因果推理。Judea Pearl 认为当今深度学习所有令人印象深刻的成就，都只不过是为了适应“曲线拟合（Curve fitting）”。而今，这也导致深度学习的研究员们困在了“关联级别”的问题窘境里。Judea  Pearl 期望能掀起一场“因果革命”，采用因果推理模型，从因果而非单纯的数据关联角度去研究人工智能。
  
 
== 什么问题 对应 Pearl 的研究，问题被解决得 怎么样？ ==
 
== 什么问题 对应 Pearl 的研究，问题被解决得 怎么样？ ==
第36行： 第42行：
  
 
Causal Bayesian Networks，Pearl 正在研究。
 
Causal Bayesian Networks，Pearl 正在研究。
 +
 +
== 近五年相关论文 ==
 +
(R-513):  [pdf]
 +
 +
S. Mueller and J. Pearl "Personalized Decision Making -- A Conceptual Introduction,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-513), April 2022.
 +
 +
(R-511):  [pdf] [bib]  
 +
 +
A. Li and J. Pearl "Bounds on Causal Effects and Application to High Dimensional Data,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-511), March 2022.
 +
 +
In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22).
 +
 +
(R-510):  [pdf] [bib]  
 +
 +
A. Li and J. Pearl "Unit Selection with Causal Diagram,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-510), March 2022.
 +
 +
In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22).
 +
 +
(R-509):  [pdf] [bib]  
 +
 +
A. Forney and S. Mueller "Causal Inference in AI Education: A Primer,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-509), June 2022.
 +
 +
Forthcoming, Journal of Causal Inference.
 +
 +
(R-508):  [pdf] [bib]  
 +
 +
C. Cinelli "Transparent and Robust Causal Inferences in the Social and Health Sciences,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-508), July 2021.
 +
 +
Ph.D. Thesis
 +
 +
(R-507):  [pdf] [bib]  
 +
 +
A. Li, "Unit Selection Based on Counterfactual Logic,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-507), June 2021.
 +
 +
Ph.D. Thesis
 +
 +
(R-506):  [pdf] [bib]  
 +
 +
S. Mueller, "Estimating Individualized Causes of Effects by Leveraging Population Data,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-506), June 2021.
 +
 +
Master's Thesis
 +
 +
(R-505):  [pdf] [bib]
 +
 +
S. Mueller, A. Li, and J. Pearl "Causes of effects: Learning individual responses from population data,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-505), Revised May 2022.
 +
 +
Forthcoming, Proceedings of IJCAI-2022. 5br>
 +
 +
(R-505-Supplemental):  [Supplemental]
 +
 +
(R-504):  [pdf] [bib]
 +
 +
C. Zhang, C. Cinelli, B. Chen, and J. Pearl "Exploiting equality constraints in causal inference,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-504), April 2021.
 +
 +
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, California, USA. PMLR: Volume 130, 1630-1638, April 2021.
 +
 +
(R-504-Supplemental):  [Supplemental]
 +
 +
(R-503):  [pdf] [bib]
 +
 +
J. Pearl "Causally Colored Reflections on Leo Breiman's `Statistical Modeling: The Two Cultures' (2001),"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-503), March 2021.
 +
 +
Observational Studies, Vol. 7.1:187-190, 2021.
 +
 +
(R-502):  [pdf] [bib]
 +
 +
J. Pearl "Radical Empiricism and Machine Learning Research,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-502), May 2021.
 +
 +
Journal of Causal Inference, 9:78–82, 2021.
 +
 +
(R-501):  [pdf] [bib]
 +
 +
J. Pearl "Causal, Casual, and Curious (2013-2020): A collage in the art of causal reasoning,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-501), October 2020.
 +
 +
(R-493):  [pdf] [bib]
 +
 +
C. Cinelli, A. Forney, and J. Pearl "A Crash Course in Good and Bad Controls,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-493), Revised, March 2022.
 +
 +
Forthcoming, Journal Sociological Methods and Research.
 +
 +
(R-492):  [pdf] [bib]
 +
 +
C. Cinelli and J. Pearl "Generalizing experimental results by leveraging knowledge of mechanisms,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-492), September 2020.
 +
 +
European Journal of Epidemiology, 36:149--164, 2021. URL <nowiki>https://doi.org/10.1007/s10654-020-00687-4</nowiki>.
 +
 +
(R-491-L):  [pdf] [bib]
 +
 +
C. Zhang, B. Chen, and J. Pearl "A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-491-L), February 2020.
 +
 +
Extended version of paper in Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI-2020), 34(6): 10318--10325, Palo Alto, CA: AAAI Press, 2020.
 +
 +
(R-489):  [pdf] [bib]
 +
 +
J. Pearl "The Limitations of Opaque Learning Machines,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-489), May 2019.
 +
 +
Chapter 2 in John Brockman (Ed.), Possible Minds: 25 Ways of Looking at AI, Penguin Press, 2019.
 +
 +
(R-488):  [pdf] [bib]
 +
 +
A. Li and J. Pearl "Unit Selection Based on Counterfactual Logic,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-488), June 2019.
 +
 +
In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), 1793-1799, 2019.
 +
 +
(R-488-Supplemental):  [Supplemental]
 +
 +
(R-487):  [pdf] [bib]
 +
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J. Pearl and Co-authored by D. Mackenzie, "Telling and re-telling history: The case for a whiggish account of the history of causation,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-487), March 2019.
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(R-486):  [pdf] [bib]
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J. Pearl, "On the interpretation of do(x),"
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UCLA Cognitive Systems Laboratory, Technical Report (R-486), February 2019.
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Journal of Causal Inference, Causal, Casual, and Curious Section, 7(1), online, March 2019.
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(R-485):  [pdf] [bib]
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J. Pearl, "Causal and counterfactual inference,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-485), December 2021.
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In Markus Knauff and Wolfgang Spohn (Eds.), The Handbook of Rationality, Section 7.1, pp. 427-438, The MIT Press, 2021.
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(R-484):  [pdf] [bib]
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J. Pearl, "Sufficient Causes: On Oxygen, Matches, and Fires,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-484), September 2019.
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Journal of Causal Inference, Causal, Casual, and Curious Section, AOP, <nowiki>https://doi.org/10.1515/jci-2019-0026</nowiki>, September 2019.
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(R-483):  [pdf] [bib]
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J. Pearl, "Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-483), August 2018.
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Journal of Causal Inference, Causal, Casual, and Curious Section, 6(2), online, September 2018.
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(R-482):  [pdf] [bib]
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C. Cinelli, D. Kumor, B. Chen, J. Pearl, and E. Bareinboim
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"Sensitivity Analysis of Linear Structural Causal Models,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-482), June 2019.
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97, 1252-1261, 2019.
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(R-481):  [pdf] [bib]
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J. Pearl, "The Seven Tools of Causal Inference with Reflections on Machine Learning,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-481), July 2018.
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Communications of ACM, 62(3): 54-60, March 2019
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(R-480):  [pdf] [bib]
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K. Mohan, F. Thoemmes, J. Pearl, "Estimation with Incomplete Data: The Linear Case,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-480), May 2018.
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Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), 5082-5088, 2018.
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(R-479):  [pdf] [bib]
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C. Cinelli and J. Pearl, "RE: A Practical Example Demonstrating the Utility of Single-world Intervention Graphs,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-479), April 2018.
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Journal of Epidemiology, 29(6): e50--e51, November 2018.
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(R-478):  [pdf] [bib]
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J. Pearl and E. Bareinboim, "A note on `Generalizability of Study Results',"
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UCLA Cognitive Systems Laboratory, Technical Report (R-478), April 2018.
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Epidemiology, 30(2):186--188, March 2019.
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(R-477):  [pdf] [bib]
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J. Pearl, "Challenging the Hegemony of Randomized Controlled Trials: Comments on Deaton and Cartwright,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-477), April 2018.
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Social Science and Medicine, published online, April 2018.
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(R-476):  [pdf] [bib]
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J. Pearl, "A Personal Journey into Bayesian Networks,"
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UCLA Cognitive Systems Laboratory, Technical Report (R-476), May 2018.
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(R-475):  [pdf] [bib]
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J. Pearl, "Theoretical Impediments to Machine Learning with Seven Sparks from the Causal Revolution"
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UCLA Cognitive Systems Laboratory, Technical Report (R-475), July 2018.
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Paper supporting Keynote Talk WSDM'18, Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, DOI: <nowiki>http://dx.doi.org/10.1145/3159652.3160601</nowiki>, February 2018.
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(R-474):  [pdf] [bib]
 +
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J. Pearl, "Comments on `The Tale Wagged by the DAG'"
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UCLA Cognitive Systems Laboratory, Technical Report (R-474), January 2018.
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International Journal of Epidemiology, 47(3):1002-1004, 2018.
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(R-473):  [pdf] [bib]
 +
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K. Mohan and J. Pearl, "Graphical Models for Processing Missing Data"
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UCLA Cognitive Systems Laboratory, Technical Report (R-473-L), June 2019.
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Journal of American Statistical Association (JASA). Online March 2021 (<nowiki>https://doi.org/10.1080/01621459.2021.1874961</nowiki>).
 +
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(R-472):  [pdf] [bib]
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J. Pearl, "What is Gained from Past Learning"
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UCLA Cognitive Systems Laboratory, Technical Report (R-472), March 2018.
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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>
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(R-471):  [pdf] [bib]
 +
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A. Forney, J. Pearl, and E. Bareinboim, "Counterfactual Data-Fusion for Online Reinforcement Learners"
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UCLA Cognitive Systems Laboratory, Technical Report (R-471), June 2017.
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Presented at the Transfer in Reinforcement Learning workshop at AAMAS-2017.
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Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1156-1164, 2017.
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(R-470):  [pdf] [bib]
 +
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J. Pearl, "The Eight Pillars of Causal Wisdom"
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UCLA Cognitive Systems Laboratory, Technical Report (R-470), April 2017.
 +
 +
(R-469):
 +
 +
J. Pearl, "A Personal Journey into Bayesian Networks,"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-476), May 2018.
 +
 +
(R-466):  [pdf] [bib]
 +
 +
J. Pearl "The Sure-Thing Principle"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-466), February 2016.
 +
 +
Journal of Causal Inference, Causal, Casual, and Curious Section, 4(1):81-86, March 2016.
 +
 +
(R-461):  [pdf] [bib]
 +
 +
B. Chen, J. Pearl, and E. Bareinboim, "Incorporating Knowledge into Structural Equation Models using Auxiliary Variables"
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UCLA Cognitive Systems Laboratory, Technical Report (R-461), July 2016.
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In S. Kambhampati (Ed.), Proceedings of the 25 International Joint Conference on Artificial Intelligence (IJCAI), Palo Alto: AAAI Press, 3577-3583, 2016.
 +
 +
(R-461-L):  [pdf]
 +
 +
B. Chen, J. Pearl, and E. Bareinboim, "Incorporating Knowledge into Structural Equation Models using Auxiliary Variables"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-461-L), April 2016.
 +
 +
Extended version of paper in S. Kambhampati (Ed.), Proceedings of the 25 International Joint Conference on Artificial Intelligence (IJCAI), Palo Alto: AAAI Press, 3577-3583, 2016.
 +
 +
(R-460):  [pdf] [bib]
 +
 +
E. Bareinboim, Andrew Forney, and J. Pearl, "Bandits with Unobserved Confounders: A Causal Approach"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-460), November 2015.
 +
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In C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, and R. Garnett (Eds.), Neural Information Processing Systems (NIPS) Conference, Advances in Neural Information Processing Systems 28, Curran Associates, Inc., pp. 1342-1350, 2015.
 +
 +
(R-459):  [pdf] [bib]
 +
 +
J. Pearl, "A Linear `Microscope' for Interventions and Counterfactuals"
 +
 +
UCLA Cognitive Systems Laboratory, Technical Report (R-459), March 2017.
 +
 +
Journal of Causal Inference, Causal, Casual, and Curious Section, published online 5(1):1-15, March 2017.
  
 
== 参考文献 ==
 
== 参考文献 ==

2022年6月17日 (五) 23:58的版本

谁是 J. Pearl ?

1936年出生

教授，工作于计算机科学系 加州大学洛杉矶分校

电话：（310）825-3243

传真：（310）825-2273

电子邮件：judea@cs.ucla.edu

J. Pearl 的网页

朱迪亚·珀尔（Judea Pearl）——以色列裔美籍计算机科学家、哲学家，以倡导人工智能的概率方法和贝叶斯网络而闻名。他还因在结构模型的基础上发展出因果和反事实推论而受到广泛称赞。2011年，ACM授予Judea Pearl图灵奖，以表彰他“通过发展概率和因果推理演算对人工智能做出的基础性贡献”。他早在40多年前便通过贝叶斯网络的设计，使机器实现概率推理而在人工智能领域声名大噪，并被誉为“贝叶斯网络之父”，但近年却公开声称自己其实是人工智能社区的一名“叛徒”：离开了主流追逐、并且也是由他奠定重要理论基础和方法论的概率推理，而去追求一项更具挑战性的任务——因果推理。Judea Pearl 认为当今深度学习所有令人印象深刻的成就，都只不过是为了适应“曲线拟合（Curve fitting）”。而今，这也导致深度学习的研究员们困在了“关联级别”的问题窘境里。Judea  Pearl 期望能掀起一场“因果革命”，采用因果推理模型，从因果而非单纯的数据关联角度去研究人工智能。

什么问题 对应 Pearl 的研究，问题被解决得 怎么样？

问题1：如何更新信念 依据不确定的信息？[1]

  • 使用经典的逻辑，推理出现例外

例如：（1）如果我家的屋顶湿，邻居家的屋顶湿

（2）如果用水浇我家的屋顶，我家的屋顶湿

从（1）和（2）推理出： 如果用水浇我家的屋顶，邻居家的屋顶湿。

可以修改为：如果我家的屋顶湿，邻居家的屋顶湿，不包括用水浇我家的屋顶。

所以逻辑 需要覆盖例外情况，这样的逻辑称为Default Logic。

  • 依据不确定的信息，如何推理出结论？

可以使用Fuzzy Logics，给结论赋予一系列可能的事实。

  • 在推理中，如何模仿信息平行地传播 在大脑中？

使用Beysian Networks, 信息在polytrees中传播。如果传播路径有环（loops）, Appropriate Method 能够高效的、准确的解决。在Appropriate method中，信息如何在loops传播，目前不知道。

  • 依据不确定的信息，怎样推理 更好？

Causal Bayesian Networks，Pearl 正在研究。

近五年相关论文

(R-513):  [pdf]

S. Mueller and J. Pearl "Personalized Decision Making -- A Conceptual Introduction,"

UCLA Cognitive Systems Laboratory, Technical Report (R-513), April 2022.

(R-511):  [pdf] [bib]  

A. Li and J. Pearl "Bounds on Causal Effects and Application to High Dimensional Data,"

UCLA Cognitive Systems Laboratory, Technical Report (R-511), March 2022.

In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22).

(R-510):  [pdf] [bib]  

A. Li and J. Pearl "Unit Selection with Causal Diagram,"

UCLA Cognitive Systems Laboratory, Technical Report (R-510), March 2022.

In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22).

(R-509):  [pdf] [bib]  

A. Forney and S. Mueller "Causal Inference in AI Education: A Primer,"

UCLA Cognitive Systems Laboratory, Technical Report (R-509), June 2022.

Forthcoming, Journal of Causal Inference.

(R-508):  [pdf] [bib]  

C. Cinelli "Transparent and Robust Causal Inferences in the Social and Health Sciences,"

UCLA Cognitive Systems Laboratory, Technical Report (R-508), July 2021.

Ph.D. Thesis

(R-507):  [pdf] [bib]  

A. Li, "Unit Selection Based on Counterfactual Logic,"

UCLA Cognitive Systems Laboratory, Technical Report (R-507), June 2021.

Ph.D. Thesis

(R-506):  [pdf] [bib]  

S. Mueller, "Estimating Individualized Causes of Effects by Leveraging Population Data,"

UCLA Cognitive Systems Laboratory, Technical Report (R-506), June 2021.

Master's Thesis

(R-505):  [pdf] [bib]

S. Mueller, A. Li, and J. Pearl "Causes of effects: Learning individual responses from population data,"

UCLA Cognitive Systems Laboratory, Technical Report (R-505), Revised May 2022.

Forthcoming, Proceedings of IJCAI-2022. 5br>

(R-505-Supplemental):  [Supplemental]

(R-504):  [pdf] [bib]

C. Zhang, C. Cinelli, B. Chen, and J. Pearl "Exploiting equality constraints in causal inference,"

UCLA Cognitive Systems Laboratory, Technical Report (R-504), April 2021.

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, California, USA. PMLR: Volume 130, 1630-1638, April 2021.

(R-504-Supplemental):  [Supplemental]

(R-503):  [pdf] [bib]

J. Pearl "Causally Colored Reflections on Leo Breiman's `Statistical Modeling: The Two Cultures' (2001),"

UCLA Cognitive Systems Laboratory, Technical Report (R-503), March 2021.

Observational Studies, Vol. 7.1:187-190, 2021.

(R-502):  [pdf] [bib]

J. Pearl "Radical Empiricism and Machine Learning Research,"

UCLA Cognitive Systems Laboratory, Technical Report (R-502), May 2021.

Journal of Causal Inference, 9:78–82, 2021.

(R-501):  [pdf] [bib]

J. Pearl "Causal, Casual, and Curious (2013-2020): A collage in the art of causal reasoning,"

UCLA Cognitive Systems Laboratory, Technical Report (R-501), October 2020.

(R-493):  [pdf] [bib]

C. Cinelli, A. Forney, and J. Pearl "A Crash Course in Good and Bad Controls,"

UCLA Cognitive Systems Laboratory, Technical Report (R-493), Revised, March 2022.

Forthcoming, Journal Sociological Methods and Research.

(R-492):  [pdf] [bib]

C. Cinelli and J. Pearl "Generalizing experimental results by leveraging knowledge of mechanisms,"

UCLA Cognitive Systems Laboratory, Technical Report (R-492), September 2020.

European Journal of Epidemiology, 36:149--164, 2021. URL https://doi.org/10.1007/s10654-020-00687-4.

(R-491-L):  [pdf] [bib]

C. Zhang, B. Chen, and J. Pearl "A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models,"

UCLA Cognitive Systems Laboratory, Technical Report (R-491-L), February 2020.

Extended version of paper in Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI-2020), 34(6): 10318--10325, Palo Alto, CA: AAAI Press, 2020.

(R-489):  [pdf] [bib]

J. Pearl "The Limitations of Opaque Learning Machines,"

UCLA Cognitive Systems Laboratory, Technical Report (R-489), May 2019.

Chapter 2 in John Brockman (Ed.), Possible Minds: 25 Ways of Looking at AI, Penguin Press, 2019.

(R-488):  [pdf] [bib]

A. Li and J. Pearl "Unit Selection Based on Counterfactual Logic,"

UCLA Cognitive Systems Laboratory, Technical Report (R-488), June 2019.

In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), 1793-1799, 2019.

(R-488-Supplemental):  [Supplemental]

(R-487):  [pdf] [bib]

J. Pearl and Co-authored by D. Mackenzie, "Telling and re-telling history: The case for a whiggish account of the history of causation,"

UCLA Cognitive Systems Laboratory, Technical Report (R-487), March 2019.

(R-486):  [pdf] [bib]

J. Pearl, "On the interpretation of do(x),"

UCLA Cognitive Systems Laboratory, Technical Report (R-486), February 2019.

Journal of Causal Inference, Causal, Casual, and Curious Section, 7(1), online, March 2019.

(R-485):  [pdf] [bib]

J. Pearl, "Causal and counterfactual inference,"

UCLA Cognitive Systems Laboratory, Technical Report (R-485), December 2021.

In Markus Knauff and Wolfgang Spohn (Eds.), The Handbook of Rationality, Section 7.1, pp. 427-438, The MIT Press, 2021.

(R-484):  [pdf] [bib]

J. Pearl, "Sufficient Causes: On Oxygen, Matches, and Fires,"

UCLA Cognitive Systems Laboratory, Technical Report (R-484), September 2019.

Journal of Causal Inference, Causal, Casual, and Curious Section, AOP, https://doi.org/10.1515/jci-2019-0026, September 2019.

(R-483):  [pdf] [bib]

J. Pearl, "Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes,"

UCLA Cognitive Systems Laboratory, Technical Report (R-483), August 2018.

Journal of Causal Inference, Causal, Casual, and Curious Section, 6(2), online, September 2018.

(R-482):  [pdf] [bib]

C. Cinelli, D. Kumor, B. Chen, J. Pearl, and E. Bareinboim

"Sensitivity Analysis of Linear Structural Causal Models,"

UCLA Cognitive Systems Laboratory, Technical Report (R-482), June 2019.

Proceedings of the 36th International Conference on Machine Learning, PMLR 97, 1252-1261, 2019.

(R-481):  [pdf] [bib]

J. Pearl, "The Seven Tools of Causal Inference with Reflections on Machine Learning,"

UCLA Cognitive Systems Laboratory, Technical Report (R-481), July 2018.

Communications of ACM, 62(3): 54-60, March 2019

(R-480):  [pdf] [bib]

K. Mohan, F. Thoemmes, J. Pearl, "Estimation with Incomplete Data: The Linear Case,"

UCLA Cognitive Systems Laboratory, Technical Report (R-480), May 2018.

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), 5082-5088, 2018.

(R-479):  [pdf] [bib]

C. Cinelli and J. Pearl, "RE: A Practical Example Demonstrating the Utility of Single-world Intervention Graphs,"

UCLA Cognitive Systems Laboratory, Technical Report (R-479), April 2018.

Journal of Epidemiology, 29(6): e50--e51, November 2018.

(R-478):  [pdf] [bib]

J. Pearl and E. Bareinboim, "A note on `Generalizability of Study Results',"

UCLA Cognitive Systems Laboratory, Technical Report (R-478), April 2018.

Epidemiology, 30(2):186--188, March 2019.

(R-477):  [pdf] [bib]

J. Pearl, "Challenging the Hegemony of Randomized Controlled Trials: Comments on Deaton and Cartwright,"

UCLA Cognitive Systems Laboratory, Technical Report (R-477), April 2018.

Social Science and Medicine, published online, April 2018.

(R-476):  [pdf] [bib]

J. Pearl, "A Personal Journey into Bayesian Networks,"

UCLA Cognitive Systems Laboratory, Technical Report (R-476), May 2018.

(R-475):  [pdf] [bib]

J. Pearl, "Theoretical Impediments to Machine Learning with Seven Sparks from the Causal Revolution"

UCLA Cognitive Systems Laboratory, Technical Report (R-475), July 2018.

Paper supporting Keynote Talk WSDM'18, Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, DOI: http://dx.doi.org/10.1145/3159652.3160601, February 2018.

(R-474):  [pdf] [bib]

J. Pearl, "Comments on `The Tale Wagged by the DAG'"

UCLA Cognitive Systems Laboratory, Technical Report (R-474), January 2018.

International Journal of Epidemiology, 47(3):1002-1004, 2018.

(R-473):  [pdf] [bib]

K. Mohan and J. Pearl, "Graphical Models for Processing Missing Data"

UCLA Cognitive Systems Laboratory, Technical Report (R-473-L), June 2019.

Journal of American Statistical Association (JASA). Online March 2021 (https://doi.org/10.1080/01621459.2021.1874961).

(R-472):  [pdf] [bib]

J. Pearl, "What is Gained from Past Learning"

UCLA Cognitive Systems Laboratory, Technical Report (R-472), March 2018.

Journal of Causal Inference, Causal, Casual, and Curious Section, 6(1), Article 20180005, March 2018. https://doi.org/10.1515/jci-2018-0005

(R-471):  [pdf] [bib]

A. Forney, J. Pearl, and E. Bareinboim, "Counterfactual Data-Fusion for Online Reinforcement Learners"

UCLA Cognitive Systems Laboratory, Technical Report (R-471), June 2017.

Presented at the Transfer in Reinforcement Learning workshop at AAMAS-2017.

Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1156-1164, 2017.

(R-470):  [pdf] [bib]

J. Pearl, "The Eight Pillars of Causal Wisdom"

UCLA Cognitive Systems Laboratory, Technical Report (R-470), April 2017.

(R-469):

J. Pearl, "A Personal Journey into Bayesian Networks,"

UCLA Cognitive Systems Laboratory, Technical Report (R-476), May 2018.

(R-466):  [pdf] [bib]

J. Pearl "The Sure-Thing Principle"

UCLA Cognitive Systems Laboratory, Technical Report (R-466), February 2016.

Journal of Causal Inference, Causal, Casual, and Curious Section, 4(1):81-86, March 2016.

(R-461):  [pdf] [bib]

B. Chen, J. Pearl, and E. Bareinboim, "Incorporating Knowledge into Structural Equation Models using Auxiliary Variables"

UCLA Cognitive Systems Laboratory, Technical Report (R-461), July 2016.

In S. Kambhampati (Ed.), Proceedings of the 25 International Joint Conference on Artificial Intelligence (IJCAI), Palo Alto: AAAI Press, 3577-3583, 2016.

(R-461-L):  [pdf]

B. Chen, J. Pearl, and E. Bareinboim, "Incorporating Knowledge into Structural Equation Models using Auxiliary Variables"

UCLA Cognitive Systems Laboratory, Technical Report (R-461-L), April 2016.

Extended version of paper in S. Kambhampati (Ed.), Proceedings of the 25 International Joint Conference on Artificial Intelligence (IJCAI), Palo Alto: AAAI Press, 3577-3583, 2016.

(R-460):  [pdf] [bib]

E. Bareinboim, Andrew Forney, and J. Pearl, "Bandits with Unobserved Confounders: A Causal Approach"

UCLA Cognitive Systems Laboratory, Technical Report (R-460), November 2015.

In C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, and R. Garnett (Eds.), Neural Information Processing Systems (NIPS) Conference, Advances in Neural Information Processing Systems 28, Curran Associates, Inc., pp. 1342-1350, 2015.

(R-459):  [pdf] [bib]

J. Pearl, "A Linear `Microscope' for Interventions and Counterfactuals"

UCLA Cognitive Systems Laboratory, Technical Report (R-459), March 2017.

Journal of Causal Inference, Causal, Casual, and Curious Section, published online 5(1):1-15, March 2017.

参考文献

[1]J. Pearl, "A Personal Journey into Bayesian Networks,"

UCLA Cognitive Systems Laboratory, Technical Report (R-476), May 2018.

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说明

J. Pearl 发表很多论文，是困难的 去编写问题 从Pearl 的论文 使用自己的语言。因此，我采用多轮次去编写。每个轮次编写1~2个问题。更多问题将编写。

  • 如何编写问题？ 请参考“V形图”
取自“https://wiki.swarma.org/index.php?title=Judea_Pearl&oldid=32355”