Title: Inferring Causal Relations between Temporal Events. Abstract: Due to the popularity of event-based data, causal inference from event datasets has attracted increasing interest. However, inferring causalities from observational event sequences is challenging because of the heterogeneous and irregular nature of event-based data. Existing work on causal inference for temporal events disregards the event durations, and is thus unable to capture their impact on the causal relations. In the present paper, we overcome this limitation by proposing a new modeling approach for temporal events that captures and utilizes event durations. Based on this new temporal model, we propose a set of novel Duration-based Event Causality (DEC) scores, including the Duration-based Necessity and Sufficiency Trade-off score, and the Duration-based Conditional Intensity Rates scores that take into consideration event durations when inferring causal relations between temporal events. We prove that the proposed scores follow the causality hypothesis testing framework.  We conduct an extensive experimental evaluation using both synthetic datasets, and two real-world event datasets in the medical and environmental domains to evaluate our proposed scores, and compare them against the closest baseline. The experimental results show that our proposed scores outperforms the baseline with a large margin using the popular evaluation metric Hits@K.