Causality Discovery with Domain Knowledge for Drug-Drug Interactions Discovery

Bayesian Network Probabilistic Graphs have recently been applied to the problem of discovery drug-drug interactions, i.e., the identification of drugs that, when consumed together, produce an unwanted side effect. These methods have the advantage of being explainable: the cause of the interaction is made explicit. However, they suffer from two intrinsic problems: (1) the high time-complexity for computing causation, i.e., exponential; and (2) the difficult identification of causality directions, i.e., it is difficult to identify in drug-drug interactions databases whether a drug causes an adverse effect - or vice versa, an adverse effect causes a drug consumption. While solutions for addressing the causality direction identification exist, e.g., the CARD method, these assume statistical independence between drug pairs considered for interaction: real data often does not satisfy this condition. In this paper, we propose a novel causality discovery algorithm for drug-drug interactions that goes beyond these limitations: Domain-knowledge-driven Causality Discovery (DCD). In DCD, a knowledge base that contains known drug-side effect pairs is used to prime a greedy drugdrug interaction algorithm that detects the drugs that, when consumed together, cause a side effect. This algorithm resolves the drug-drug interaction discovery problem in O(n(2)) time and provides the causal direction of combined causes and their effect, without resorting to assuming statistical independence of drugs intake. Comprehensive experiments on realworld and synthetic datasets show the proposed method is more effective and efficient than current state-of-the-art solutions, while also addressing a number of drawbacks of current solutions, including the high time complexity, and the strong assumptions regarding real-world data that are often violated.