Organizers: Erica E.M. Moodie (McGill), David A. Stephens (McGill), Alexandra M. Schmidt (McGill)
The goal of most, if not all, statistical inference is to uncover causal relationships, however it is not generally possible to infer causality from standard statistical procedures. In the last three decades, the field of causal inference research has grown at a rapid pace, and yet much of the literature is devoted to relatively simple settings. In this month-long program, we aim to push the frontiers of causal inference beyond simple settings to accommodate complex data with features such as network or spatial structure. We will hold a series of lectures and workshops that address current and novel aspects of causal inference, which involves the uncovering of relationships between variables in an observationally-derived data collection setting. Throughout this program, we will investigate new and challenging settings that have been studied in the conventional statistical literature, but not viewed through the lens of causal inference. The unifying theme of the program is that of complex dependence, with a particular focus on spatial, network, and graphical structures.