Search Result Explanation (SeRE) aims to improve search sessions' effectiveness and efficiency by helping users interpret documents' relevance. Existing works mostly focus on factual explanation, i.e. to find/generate supporting evidence about documents' relevance to search queries. However, research in cognitive sciences has shown that human explanations are contrastive i.e. people explain an observed event using some counterfactual events; such explanations reduce cognitive load and provide actionable insights. Though already proven effective in machine learning and NLP communities, there lacks a strict formulation on how counterfactual explanations should be defined and structured, in the context of web search. In this paper, we first discuss the possible formulation of counterfactual explanations in the IR context. Next, we formulate a suite of desiderata for counterfactual explanation in SeRE task and corresponding automatic metrics. With this desiderata, we propose a method named \textbf{C}ounter\textbf{F}actual \textbf{E}diting for Search Research \textbf{E}xplanation (\textbf{CFE2}). CFE2 provides pairwise counterfactual explanations for document pairs within a search engine result page. Our experiments on five public search datasets demonstrate that CFE2 can significantly outperform baselines in both automatic metrics and human evaluations.

翻译：搜索结果的解释（SeRE）旨在通过帮助用户理解文档与查询的相关性，提升搜索会话的效果与效率。现有研究主要关注事实性解释，即寻找或生成文档与搜索查询相关的支持性证据。然而，认知科学领域的研究表明，人类的解释具有对比性——即人们通过因果反事实事件来解释观察到的现象；这类解释能降低认知负荷并提供可操作的见解。尽管因果反事实解释在机器学习和自然语言处理领域已被证明有效，但在网页搜索场景中，关于如何定义与构建因果反事实解释仍缺乏严谨的框架。本文首先探讨了信息检索场景下因果反事实解释的可能形式，随后针对搜索解释任务制定了一套理想化准则及对应的自动评估指标。基于该准则，我们提出了一种名为**CFE2**（因果反事实编辑用于搜索解释）的方法。CFE2能为搜索引擎结果页面中的文档对提供成对的因果反事实解释。我们在五个公开搜索数据集上的实验表明，CFE2在自动评估指标和人工评价中均显著优于基线方法。