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Improving Causal Learning Scalability and Performance using Aggregates and Interventions

Published: 22 September 2023 Publication History

Abstract

Smart homes are Cyber-Physical Systems (CPS) where multiple devices and controllers cooperate to achieve high-level goals. Causal knowledge on relations between system entities is essential for enabling system self-adaption to dynamic changes. As house configurations are diverse, this knowledge is difficult to obtain. In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows. Starting with considering all possible relations, we progressively discarded non-correlated variables. Next, we identified causal relations from the remaining correlations by employing “do-operations.” The obtained CBN could then be employed for causal inference. The main challenges of this approach included “non-doable variables” and limited scalability. To address these issues, we propose three extensions: (i) early pruning weakly correlated relations to reduce the number of required do-operations, (ii) introducing aggregate variables that summarize relations between weakly coupled sub-systems, and (iii) applying the method a second time to perform indirect do interventions and handle non-doable relations. We illustrate and evaluate the efficiency of these contributions via examples from the smart home and power grid domain. Our proposal leads to a decrease in the number of operations required to learn the CBN and in an increased accuracy of the learned CBN, paving the way toward applications in large CPS.

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Cited By

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  • (2024)CIRCE: a Scalable Methodology for Causal Explanations in Cyber-Physical Systems2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)10.1109/ACSOS61780.2024.00026(81-90)Online publication date: 16-Sep-2024

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems
ACM Transactions on Autonomous and Adaptive Systems  Volume 18, Issue 3
September 2023
107 pages
ISSN:1556-4665
EISSN:1556-4703
DOI:10.1145/3624976
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 September 2023
Online AM: 25 July 2023
Accepted: 30 May 2023
Revised: 12 May 2023
Received: 28 March 2022
Published in TAAS Volume 18, Issue 3

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Author Tags

  1. Causal relations and knowledge
  2. causal bayesian networks
  3. do-operations
  4. scalability
  5. aggregate variables
  6. self-adaptation
  7. cyber-physical systems

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  • (2024)CIRCE: a Scalable Methodology for Causal Explanations in Cyber-Physical Systems2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)10.1109/ACSOS61780.2024.00026(81-90)Online publication date: 16-Sep-2024

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