Improving Causal Learning Scalability and Performance using Aggregates and Interventions

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.