E x CAR : Event Graph Knowledge Enhanced Explainable Causal Reasoning (2021)

Abstract: Prior work infers the causation between events mainly based on the knowledge induced from the annotated causal event pairs. However, additional evidence information intermediate to the cause and effect remains unexploited. By incorporating such information, the logical law behind the causality can be unveiled, and the interpretability and stability of the causal reasoning system can be improved. To facilitate this, we present an E vent graph knowledge enhanced e x plainable CA usal R easoning framework ( ExCAR ). ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning. To learn the conditional probabilistic of logical rules, we propose the Conditional Markov Neural Logic Network (CMNLN) that combines the representation learning and structure learning of logical rules in an end-to-end differentiable manner. Experimental results demonstrate that ExCAR outperforms previous state-of-the-art methods. Adversarial evaluation shows the improved stability of ExCAR over baseline systems. Human evaluation shows that ExCAR can achieve a promising explainable performance.

## introduction causal reasoning aims at understanding the general causal dependency between the cause and effect @xcite . causality is commonly expressed by humans in the text of natural language, and is of great value for various artificial intelligence applications, such as question answering @xcite , event prediction @xcite , and decision making @xcite . previous work mainly learns causal knowledge from manually annotated causal event pairs, and achieves promising performances @xcite ; xie and mu, 2019a; @xcite . however, recent works have questioned the seemingly superb performance for some of these studies @xcite @xcite . specifically, training data may contain exploitable superficial cues that are correlative of the expected output. the main concern is that these works have not learned the underlying mechanism of causation so that their inference models are not stable enough and their results are not explainable. while we notice that there is plentiful evidence information outside the given corpus that can provide more clues for understanding the logical law of the causality. figure 1 (a) exemplifies two clues i 1 : excess liquidity and i 2 : invest demand increase for explaining how a: quantitative easing gradually leads to b: house price increases. without these important evidence information, on the other hand, as illustrated in figure 1 (b) , the causal relationship between a, d and between c, b could not be deducted from the known causation between a, b and between c, d . in contrast, with intermediate event i in hand, according to the transitivity of causality @xcite , the logic chain of a ⇒ i ⇒ d and c ⇒ i ⇒ b could be naturally derived from the observed logic chain a ⇒ i ⇒ b and c ⇒ i ⇒ d . to fully exploit the potential of the evidence information, we present an event graph knowledge enhanced explainable causal reasoning (excar) framework. in particular, as illustrated in figure 1 (c), given an input event pair c, e , excar firstly retrieves external evidence events such as i 1 , i 2 from a large-scale causal event graph (ceg, a causal knowledge base constructed by us), and defines the causation between c, i 1 , i 2 , e as a set of logical rules (e.g., r @xmath0, which rules are useful representations for the causal reasoning task because they are interpretable and can provide insight to inference results. @xcite pointed out that the underlying logic of causality is a probabilistic logic. the advantage of using a probabilistic logic is that by equipping logical rules with probability, one can better model statistically complex and noisy data. however, learning such probabilistic logical rules in the causal reasoning scenario is quite difficult --it requires modeling the superimposed causal effect for each logical rule. different from firstorder logical rules induced from some knowledge graphs, the probability of the logical rule (i.e. the causal strength of the cause-effect pair) in causal reasoning is uncertain, which varies with different antecedents. for example, as shown in figure 1 (d ), with the antecedent a: catch a cold, a fever can hardly lead to life danger. while if fever is caused by the antecedent b: septicemia, it can result in life danger with a high probability. to address this issue, we further propose a conditional markov neural logic network (cmnln) for learning the conditional causal dependency of logical rules in an end-to-end fashion. specifically, cmnln first decomposes the logical rules set derived from the ceg into several distinct logic chains and learns a distributed representation for each logic chain in an embedding space. subsequently, cmnln estimates the conditional probability of each logical rule by an antecedent-aware potential function. then cmnln computes the probability of each logic chain by multiplying the probabilities of logical rules in the chain. finally, cmnln predicts the causality score of the input event pair based on the disjunction of chain-level causality information. experimental results show that our approach can effectively utilize the event graph information to improve the accuracy of causal reasoning by more than 5%. adversarial evaluation and human evaluation show that excar can achieve stable and explainable performance. the code is released at https://github.com/sjcfr/excar . ## method as shown in figure 2 , excar consists of two components. given an event pair c, e , excar employs an evidence retrieval module to retrieves evidence events from a prebuilt causal event graph to generate a set of logical rules. then excar conducts causal reasoning based on the logical rules using a conditional markov neural logic network. ## conclusion we devise a novel explainable causal reasoning framework excar. given an event pair, excar is able to obtain logical rules from a large-scale causal event graph to provide insight to inference results. to learn the conditional probabilistic of logical rules, we propose a conditional markov neural logic network that combines the strengths of rulebased and neural models. empirically, our method outperforms prior work on two causal reasoning datasets, including copa and c-copa. furthermore, excar is interpretable by providing explanations in terms of probabilistic logical rules.