Conférencier: Russell Steele (Université McGill) 

Most modern causal statistical methodological challenges result from analyzing data coming from dynamic causal systems. Standard approaches for time-varying confounding, non-compliance, mediation, moderation, dynamic regimes, and many others extend point exposure models and counterfactuals to the repeated observation setting. However, these models become quite strained under certain types of data generating mechanisms. In this mostly foundational seminar, I will discuss some recent work where my colleagues and I have tried to use different ways to identify interventions that lead to not only more precise definitions of effects, but also would aid in designing data collection and, potentially, different strategies for estimation.  Our work leverages research from statistics and philosophy to identity potential issues with standard assumptions and to propose new ways of defining causal effects for models which are challenging to examine using standard approaches.This work has been done in conjunction with Dr. Naftali Weinbeger (Munich Center for Mathematical Philosophy) and Dr Ian Shrier (McGill and the Lady Davis Institute).