February 19, 2025 @ 12:00 PM EST

Event Description:
Quantitative researchers working in causal inference generally have a broadly common understanding about what we mean by “causal inference,” at least with respect to estimating causal effects. Many statistical methods have been developed to estimate causal effects in individual studies, and there is a growing literature on methods for combining (or “integrating”) multiple data sources together. However, it is unclear how these advances and frameworks fit in terms of broader discussions of “causality” in science, especially for broad scientific questions that require synthesis of a wide variety of types of evidence, ranging from biological mechanistic knowledge to narrow randomized experiments to large-scale non-experimental studies, and even medical case histories. This talk will discuss lessons learned about “causality” from serving on National Academies panels, in particular one assessing a framework for “causality” used by the Environmental Protection Agency to establish potential links between exposures and health and ecological outcomes, and another that aimed to assess the literature on possible links between antimalarial exposure and long-term psychiatric symptoms among Veterans. The talk will describe the scientific contexts and lessons for us as statisticians to ensure our work is relevant and useful for such broad scientific questions.