Causal structure learning with continuous variables in continuous time

Zach Davis, Neil Bramley, Bob Rehder

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Interventions, time, and continuous-valued variables are all potentially powerful cues to causation. Furthermore, when observed over time, causal processes can contain feedback and oscillatory dynamics that make inference hard. We present a generative model and framework for causal inference over continuous variables in continuous time based on Ornstein-Uhlenbeck processes. Our generative model produces a stochastic sequence of evolving variable values that manifest many dynamical properties depending on the nature of the causal relationships, and a learner's interventions (manual changes to the values of variables during a trial). Our model is also invertible, allowing us to benchmark participant judgments against an optimal model. We find that when interacting with systems acting according to this formalism people directly compare relationships between individual variable pairs rather than considering the full space of possible models, in accordance with a local computations model of causal learning (e.g., Fernbach & Sloman, 2009). The formalism presented here provides researchers in causal cognition with a powerful framework for studying dynamic systems and presents opportunities for other areas in cognitive psychology such as control problems.
Original languageEnglish
Title of host publicationProceedings of the 40th Annual Conference of the Cognitive Science Society
Place of PublicationAustin, TX
PublisherCognitive Science Society
ISBN (Print)9780991196784
Publication statusPublished - 31 Dec 2018
Event40th Annual Meeting of the Cognitive Science Society - Madison, United States
Duration: 25 Jul 201828 Jul 2018


Conference40th Annual Meeting of the Cognitive Science Society
Abbreviated titleCogSci 2018
Country/TerritoryUnited States
Internet address

Keywords / Materials (for Non-textual outputs)

  • continous time
  • intervention
  • continous variables
  • casual learning


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