Causal structure learning over time: Observations and interventions

被引:44
|
作者
Rottman, Benjamin M. [1 ]
Keil, Frank C. [2 ]
机构
[1] Univ Chicago, Dept Hosp Med, Chicago, IL 60637 USA
[2] Yale Univ, Dept Psychol, New Haven, CT 06520 USA
关键词
Causal structure; Causal learning; Time; Observation; Intervention; DYNAMIC DECISION-MAKING; SYSTEM;
D O I
10.1016/j.cogpsych.2011.10.003
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent - the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal structures quickly and reliably when variables are temporally stable (Experiments 1 and 2). People use this strategy even when the cover story suggests that the trials are independent (Experiment 3). When observing variables over time, people believe that when a cause changes state, its effects likely change state, but an effect may change state due to an exogenous influence in which case its observed cause may not change state at the same time. People used this strategy to learn the direction of causal relations and a wide variety of causal structures (Experiments 4-6). Finally, considering exogenous influences responsible for the observed changes facilitates learning causal directionality (Experiment 7). Temporal reasoning may be the norm rather than the exception for causal learning and may reflect the way most events are experienced naturalistically. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:93 / 125
页数:33
相关论文
共 50 条
  • [1] Connecting Causal Events: Learning Causal Structures Through Repeated Interventions Over Time
    Rottman, Benjamin M.
    Keil, Frank C.
    COGNITION IN FLUX, 2010, : 907 - 912
  • [2] Time in Causal Structure Learning
    Bramley, Neil R.
    Gerstenberg, Tobias
    Mayrhofer, Ralf
    Lagnado, David A.
    JOURNAL OF EXPERIMENTAL PSYCHOLOGY-LEARNING MEMORY AND COGNITION, 2018, 44 (12) : 1880 - 1910
  • [3] The role of learning data in causal reasoning about observations and interventions
    Meder, Bjoern
    Hagmayer, York
    Waldmann, Michael P.
    MEMORY & COGNITION, 2009, 37 (03) : 249 - 264
  • [4] The role of learning data in causal reasoning about observations and interventions
    Bjöörn Meder
    York Hagmayer
    Michael R. Waldmann
    Memory & Cognition, 2009, 37 : 249 - 264
  • [5] Active causal structure learning in continuous time
    Gong, Tianwei
    Gerstenberg, Tobias
    Mayrhofer, Ralf
    Bramley, Neil R.
    COGNITIVE PSYCHOLOGY, 2023, 140
  • [6] Generative Interventions for Causal Learning
    Mao, Chengzhi
    Cha, Augustine
    Gupta, Amogh
    Wang, Hao
    Yang, Junfeng
    Vondrick, Carl
    2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, : 3946 - 3955
  • [7] Inferring causal networks from observations and interventions
    Steyvers, M
    Tenenbaum, JB
    Wagenmakers, EJ
    Blum, B
    COGNITIVE SCIENCE, 2003, 27 (03) : 453 - 489
  • [8] Learning and Testing Causal Models with Interventions
    Acharya, Jayadev
    Bhattacharyya, Arnab
    Daskalakis, Constantinos
    Kandasamy, Saravanan
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [9] Learning Causal Graphs with Small Interventions
    Shanmugam, Karthikeyan
    Kocaoglu, Murat
    Dimakis, Alexandros G.
    Vishwanath, Sriram
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 28 (NIPS 2015), 2015, 28
  • [10] Causal Structure Learning in Process Engineering Using Bayes Nets and Soft Interventions
    Kuehnert, Christian
    Bernard, Thomas
    Frey, Christian
    2011 9TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2011,