  Causal chain reasoning (CCR) is an essential ability for many decision-making
AI systems, which requires the model to build reliable causal chains by
connecting causal pairs. However, CCR suffers from two main transitive
problems: threshold effect and scene drift. In other words, the causal pairs to
be spliced may have a conflicting threshold boundary or scenario. To address
these issues, we propose a novel Reliable Causal chain reasoning
framework~(ReCo), which introduces exogenous variables to represent the
threshold and scene factors of each causal pair within the causal chain, and
estimates the threshold and scene contradictions across exogenous variables via
structural causal recurrent neural networks~(SRNN). Experiments show that ReCo
outperforms a series of strong baselines on both Chinese and English CCR
datasets. Moreover, by injecting reliable causal chain knowledge distilled by
ReCo, BERT can achieve better performances on four downstream causal-related
tasks than BERT models enhanced by other kinds of knowledge.
