This paper describes an overview of the findings of the track named Causality-driven Ad hoc Information Retrieval' (abbv. CAIR) at the Forum for Information Retrieval Evaluation (FIRE) 2020. The purpose of the track was to investigate how effectively can search systems retrieve documents that are causally related to a specified query event. Different from standard information retrieval (IR), the criteria of relevance in this search scenario is stricter in the sense that the retrieved documents at the top ranks should provide information on the potentially relevant causes that might have caused a given query event, e.g. retrieve documents on political situations that might have led to Brexit'. We released a dataset comprised of a set of 25 queries split into train and test sets. We received submissions from two participating groups. The two main observations from the best performing runs from the two participating groups are that longer queries showed a general trend to yield more causally relevant documents towards top ranks as seen from the results obtained from the first participating group, whereas it turned out that sequence-based text representation for semantically matching the documents with queries did not yield effective retrieval results, thus leaving the scope to develop supervised or semi-supervised methods to address causality-based retrieval.