Investigating Counterclaims in Causality Extraction from Text
Many causal claims, such as "sugar causes hyperactivity," are disputed or outdated. Yet research on causality extraction from text has almost entirely neglected counterclaims of causation. To close this gap, we conduct a thorough literature review of causality extraction, compile an extensive inventory of linguistic realizations of countercausal claims, and develop rigorous annotation guidelines that explicitly incorporate countercausal language. We also highlight how counterclaims of causation are an integral part of causal reasoning. Based on our guidelines, we construct a new dataset comprising 1028 causal claims, 952 counterclaims, and 1435 uncausal statements, achieving substantial inter-annotator agreement (Cohen's $\kappa = 0.74$). In our experiments, state-of-the-art models trained solely on causal claims misclassify counterclaims more than 10 times as often as models trained on our dataset.