Identifying independencies in causal graphs with feedback

被引：0
|
作者：
Pearl, J
Dechter, R
机构：
来源：
UNCERTAINTY IN ARTIFICIAL INTELLIGENCE | 1996年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We show that the d-separation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables.
引用
收藏
页码：420 / 426
页数：7
相关论文
共 50 条
  • [1] Causal Gene Selection Using Marginal Independencies
    Alipourfard, Borzou
    Gao, Jean
    2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2020, : 408 - 411
  • [2] MATRIX REPRESENTATIONS AND INDEPENDENCIES IN DIRECTED ACYCLIC GRAPHS
    Marchetti, Giovanni M.
    Wermuth, Nanny
    ANNALS OF STATISTICS, 2009, 37 (02): : 961 - 978
  • [3] Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes
    Mathur, Saurabh
    Karanam, Athresh
    Radivojac, Predrag
    Haas, David M.
    Kersting, Kristian
    Natarajan, Sriraam
    BIOCOMPUTING 2023, PSB 2023, 2023, : 359 - 370
  • [4] Recovering Causal Structures from Low-Order Conditional Independencies
    Wienoebst, Marcel
    Liskiewicz, Maciej
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 10302 - 10309
  • [5] Identifying causal effects in maximally oriented partially directed acyclic graphs
    Perkovic, Emilija
    CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI 2020), 2020, 124 : 530 - 539
  • [6] On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback
    Neal, Radford M.
    Journal of Artificial Intelligence Research, 2001, 12 (00):
  • [7] On deducing conditional independence from d-separation in causal graphs with feedback
    Neal, RM
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2000, 12 : 87 - 91
  • [8] From Causal Pairs to Causal Graphs
    Rashid, Rezaur
    Chowdhury, Jawad
    Terejanu, Gabriel
    2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA, 2022, : 802 - 807
  • [9] Dataflow graphs as complete causal graphs
    Paleyes, Andrei
    Guo, Siyuan
    Scholkopf, Bernhard
    Lawrence, Neil D.
    2023 IEEE/ACM 2ND INTERNATIONAL CONFERENCE ON AI ENGINEERING - SOFTWARE ENGINEERING FOR AI, CAIN, 2023, : 7 - 12
  • [10] Causal Discovery via Causal Star Graphs
    Zhao, Boxiang
    Wang, Shuliang
    Chi, Lianhua
    Li, Qi
    Liu, Xiaojia
    Geng, Jing
    ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2023, 17 (07)
12345