Causal Inference for Environment-Health Studies: A Survey of Recent Literature

Climate Change is perhaps the defining health challenge of our generation. Yet, despite widespread awareness and prominence, clime change's health impacts are notoriously hard to estimate. This is partly because, after all, we only have one planet, and experimenting with climate change is not possible. There is a critical role for using state-of-the-art methods for causal inference using observational data in clarifying and quantifying the importance of climate change. This seminar accompanies the growing body of research on methodological approaches to estimating climate-health impacts, and surveys recent econometric and statistical methods for causal inference using observational data, including two-way fixed effects, difference-in-differences, and doubly robust estimations. The course is designed as a seminar series for graduate students with prior expertise and interest in inferential methods for climate-health research. Each week will focus on a different research methodology, with a discussant and synthesis of approaches for applied studies.