Event causality identification (ECI) primarily involves discerning causal relations between pairs of events within sentences. However, previous methods heavily rely on large volumes of high-quality annotated data, making them impractical in low-resource scenarios. Moreover, traditional methods often make independent predictions about event pairs, ignoring the influence between these relations, leading to incorrect predictions. We propose a low-resource ECI method with global consistency constraints to address these challenges. Our approach consists of two strategies: first, we efficiently utilize high-quality data through combined domain adaptation adversarial training and semi-supervised methods with external data sources. Second, we incorporate global consistency constraints into the training process, enhancing the model’s ability to learn chain causal relations. Our method significantly improves the F1 score on the 10% Event Storyline Corpus (ESC) and 5% ESC extending the manually annotated relations within document event co-reference chains with external Causal News Corpus (CNC) and noisy causal data from Wikipedia (WNC) compared to the baseline. The addition of global consistency constraints further increases the model’s prediction consistency.