Causal Inference
Causal inference is increasingly being recognized as a crucial part of science and society. Indeed, understanding cause-effect relationships – rather than mere associations – is the primary goal in many, if not most, scientific fields. Causal inference is a broad discipline, intersecting with not only statistics and machine learning, but medicine, philosophy, public policy, and much more. Important contributions can be made in applications, methods, theory, and everywhere in between.
The Causal Inference Working Group at CMU started in 2016. We meet weekly to discuss our own research or interesting papers, both new and old; members come from communities in Statistics & Data Science, Machine Learning, Information Systems & Public Policy, Philosophy, Epidemiology, and beyond.
