Abstract: Existing methods for event causality identification (ECI) focus on mining potential causal signals, i.e., causal context keywords and event pairs. However, causal signals are ambiguous, which may lead to the context-keywords bias and the event-pairs bias. To solve this issue, we propose the \textit{counterfactual reasoning} that explicitly estimates the influence of context keywords and event pairs in training, so that we are able to eliminate the biases in inference. Experiments are conducted on two datasets, the result demonstrates the effectiveness of our method.