Meeting in the Middle: Systems Science and Causal Inference Models
In recent years, both causal inference methods and systems science models have emerged as powerful new techniques with which to conduct epidemiological research. Originating from different academic traditions, using different toolsets but both attempting to study counterfactual outcomes, systems science and causal inference are often cast in opposition to one another, as if to suggest there is a clear “correct” choice of method. However, it is possible to view them as mutually strengthening. Systems science models can be used to look beyond a single study’s data to better inform causal inference models, and causal inference models, in turn, can be used to ground systems science models with rigorous, well-constructed parameter estimates. This session seeks to explore this interface, where causal inference and systems science need not be in opposition to one another.
Jess Edwards, University of North Carolina, Chapel Hill
Kristen Rappazzo, U.S. Environmental Protection Agency
Eric Lofgren, Virginia Tech
Eleanor Murray, Harvard T.H. Chan School of Public Health
Miguel Hernan, Harvard T.H. Chan School of Public Health