  Open relation extraction aims to cluster relation instances referring to the
same underlying relation, which is a critical step for general relation
extraction. Current OpenRE models are commonly trained on the datasets
generated from distant supervision, which often results in instability and
makes the model easily collapsed. In this paper, we revisit the procedure of
OpenRE from a causal view. By formulating OpenRE using a structural causal
model, we identify that the above-mentioned problems stem from the spurious
correlations from entities and context to the relation type. To address this
issue, we conduct \emph{Element Intervention}, which intervenes on the context
and entities respectively to obtain the underlying causal effects of them. We
also provide two specific implementations of the interventions based on entity
ranking and context contrasting. Experimental results on unsupervised relation
extraction datasets show that our methods outperform previous state-of-the-art
methods and are robust across different datasets.
