BiCaution: Bridging Forward–Backward Interventional and Counterfactual Causal Establishment for Event Graph-Based Abductive Reasoning

Many complex systems like our society can be considered as the dependency among a series of events, where the event graph can properly depict the uncertainty by branches aggregating into or separating from event nodes. Consequently, targeting the decisive node in event graph as the plausible hypothesis to explain causality on occurrence between indirectly connected events (i.e.,event abductive reasoning) can facilitate evolutionary pattern mining. Previous works focused on extrapolating the best hypothesis based on observed associations between events. However, causal observations at higher causality levels that can provide additional causal information are still underutilized. To obtain more complete causal establishment on the whole event graph, we propose a more challenging task called the event graph-based abductive reasoning (EGAR), which may suffer fromnonmonotonicdefeasibility (i.e., seem to be correct may not necessarily be right), along with the chain-based transition problem due to the varying contexts between intermediary nodes. Therefore, we proposeBiCautionto resolve EGAR task and thoroughly mine thelatent causality in graph. Specifically, we model the abstract probabilistic graph as prototype, on which the pearl causal hierarchy (PCH) has been imposed across the causation ladder ofassociational,interventional, andcounterfactual. Thecore innovationofBiCautionlies in its “three-anchor, multijump” mechanism, which navigatesforward/backwardpaths by projecting event triples to different causality levels, and aggregating new observations on causal establishment into the representation of original triples to be scored. Benefiting from the above principle of in-graph multilayer observation on causal establishment, our proposedBiCautioncan not only outperform existing counterparts on graph-based event abductive reasoning in different graph sizes but can also resist different causality “error” characteristics, with the capacity to process long-chain event abductive reasoning.