<p>Text matching is considered a vital task in natural language processing and constitutes a fundamental component of many NLP applications. In practical scenarios, there exist numerous texts with causal relationships, such as questions and answers in question-answering systems. For text matching tasks involving causal relationships, considering the causal connections between text pairs is of paramount importance. Furthermore, when dealing with lengthy texts, causal signals often exhibit sparsity, rendering the extraction of causal features a challenging endeavor. To address the aforementioned issues, this paper introduces an approach that amalgamates causal knowledge distillation and causal semantic extraction, denoted as the Method for Matching Texts with Causal Relationship (MTCR). This framework effectively learns deep semantic representations of causal relationships between texts. MTCR excels at handling text matching tasks that involve causal relationships, including tasks like natural language inference and answer selection. Simultaneously, it effectively identifies instances of causal inversions. Through experimentation on five benchmark text matching datasets, our research findings indicate that the proposed method can effectively handle text matching tasks involve causal relationships.</p>