Traditional information retrieval systems are primarily focused on finding topically-relevant documents, which are descriptive of a particular query concept. However, when working with sources such as collections of news articles, a user might often want to identify not only those documents which describe a news event, but also documents which explain the chain of events which potentially led to that event occurring. These associations might be complex, involving a number of causal factors. Motivated by this information need, we formulate the task of causal information retrieval. We provide a literature survey on causality-related research, and explain how the proposed task differs from standard retrieval problems. We then empirically investigate the ability of popular retrieval methods to successfully retrieve causally-relevant documents. Our results demonstrate that the performance of traditional methods are not upto the mark for this task, and that causal information retrieval remains an open challenge which is worthy of further research.