A Causal Approach for Counterfactual Reasoning in Narratives (2024)

Abstract: Counterfactual reasoning in narratives requires predicting how alternative conditions, contrary to what actually happened, might have resulted in different outcomes.One major challenge is to maintain the causality between the counterfactual condition and the generated counterfactual outcome. In this paper, we propose a basic VAE module for counterfactual reasoning in narratives. We further introduce a pre-trained classifier and external event commonsense to mitigate the posterior collapse problem in the VAE approach, and improve the causality between the counterfactual condition and the generated counterfactual outcome. We evaluate our method on two public benchmarks. Experiments show that our method is effective.

## introduction counterfactual reasoning in narratives (crn) is commonly known as predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes @xcite . specifically, given the observed narrative @xmath0, x, y), where c, x, and y denote the context, condition, and outcome, respectively, crn considers how y ′ would be if keeping the context c unchanged while perturbing x to a similar but different x ′ . figure 1 presents a case of crn. even though it is considered a crucial component of intelligent systems @xcite , only a few resources have been devoted to crn. some of the works @xcite @xcite design dataset-specific heuristic methods, but they are actually abusing unique patterns, i.e., the feature of minimum editing, in the dataset, which limits the generality of their methods. other works @xcite take advantage of the figure 1 : an example of counterfactual reasoning in narratives. the example comes from timetravel @xcite . the colored text in the counterfactual outcome denotes the modified parts. generally, crn relies on the ability to find causality in narratives @xcite , i.e., y ′ should express a clear causal relation to x ′ to make it clear how the perturbation makes the observed outcome change. this problem naturally fits to be formulated with a causal mechanism @xcite , which requires us to infer the background knowledge that is compatible with (c, x ′ , y ′ ). however, this is non-trivial as it involves estimating the posterior of the background knowledge. luckily, with the variational technique (kingma and welling, 2013), we are able to use the background compatible with the observed s to approximate the posterior distribution. in fact, the variational process provides an approximation of the background of (c, x ′ , y ′ ), but it may face the problem of posterior collapse @xcite . as a result, the generated y ′ may not be the precise effect of x ′ , and the resulting model may be sub-optimal. to mitigate this problem, we further propose two intuitive strategies, which introduce a pre-trained classifier and commonsense causality, to enhance the causality between (c, x ′ ) and the generated y ′ . in this work, we propose a causal approach for crn. we utilize the variational process to approximate the implicit background of counterfactual scenarios. in addition, we devise two strategies to alleviate the posterior collapse problem in this variational process. first, inspired by research on natural language inference @xcite , we want to ensure that the generated y ′ entails its true condition x ′ . in other words, the model should correctly learn the influence of the condition on the outcome. therefore, we introduce a pretrained classifier that estimates the likelihood of a text y entails an input (c, x). we use the gumbelsoftmax technique @xcite to enable gradient back-propagation. second, we exploit comet @xcite to retrieve diverse event causality tailored for (c, x ′ ), which allows for deducing plausible event sequences and provides an explicit background for the unobserved counterfactual outcome y ′ . to summarize, we formulate crn in a variational framework and introduce event causality and a pre-trained classifier to further improve the causality between x ′ and the generated y ′ . our method is a general approach that is applicable to multiple tasks. the experiment proves the effectiveness of our method. we also study the practicality of the generated counterfactual narratives via a data augmentation experiment. ## related work causality for nlp the research of causality aims to explore the causal relationships in the data @xcite . recently, there has been a strong interest in utilizing causal inference to enhance current natural language understanding and generation. these works are mainly studied in event detect @xcite ), text classification @xcite , relation extraction @xcite , etc. another line of research attempts to equip the current text generation with counterfactual reasoning ability. these works involve fields such as dialogue generation @xcite , machine translation @xcite , style transferring @xcite , etc. yet there have been few works that apply causal perspective to counterfactual reason-ing in narratives. we adapt the ideas of the above works and propose additional strategies to improve the causality between the counterfactual condition and the generated counterfactual outcome. ## conclusion in this work, we formulate counterfactual reasoning in narratives in a vae framework. in addition, we introduce a pre-train classifier and external event causality to further improve the causality between the counterfactual condition and the generated counterfactual outcome. the experiment proves the effectiveness of our method. we also conduct a data augmentation experiment to verify the practicality of our method.