Data-driven discovery of causal interactions
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Causal discovery is a primary focus in many fields. Various methods have been developed to mine causal relationships from observational data. Most of the methods are only capable of identifying individual causes without considering their interactions. However, in real life, many effects are due to multiple factors that interact with each other. Therefore, detecting the interactions between those causal factors is essential for understanding the real causal mechanisms. So far, there are no efficient data-driven approaches to discovering causal interactions from data, especially large data sets. In this paper, we propose a general data-driven framework and develop four algorithms instantiated from the framework to detect causal interactions, directly from data. Extensive experiments on both synthetic and real-world data have shown that the proposed framework and the algorithms can achieve high effectiveness and efficiency for causal interaction discovery.
KeywordsCausal discovery Potential outcome Causal interactions
This work has been partially supported by Australian Research Council (ARC) Discovery grant DP140103617 and ARC Discovery grant DP170101306.
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