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Yale Causal Inference and Reasoning Working Group (YCIR): Home

Yale Causal Inference and Reasoning Working Group (YCIR) provides a multidisciplinary forum for students, faculties, and researchers to join efforts and grow ideas and methods to address the challenge of causal inference

Yale Causal Inference and Reasoning Working Group (YCIR) provides a multidisciplinary forum for students, faculties, and researchers to join efforts and to grow ideas and methods to address the challenge of causal inference. Spanning considerations from computational models to the perception and the interpretation of causality, YCIR hopes to spark dialog among students, professors, and researchers in the field. Our goal is also to bridge the gap between disciplines such as data science, artificial intelligence, biology, Epidemiology, computational physics, social sciences, neuroscience, psychology, philosophy, and others. Through bridging this gap, experts of each field can learn and use the set of causal inference definitions and methods that have been developed in other areas. Also, YCIR aspires to serve as a foundation for short-term projects and long-term comprehensive collaborations and to cultivate meaningful contribution to advance the science and philosophy of causal inferences and reasoning. We will organize lectures, group meetings, interdisciplinary projects, and workshops to foster such partnerships.

Why causal inference matters

Inferring causal relationships is a key premise of scientific research across all disciplines. Indeed, most of the questions that motivate research are causal by nature*. However, despite its importance, we have not invested enough in pursuing better definitions of causality and means to identify and measure it. K. J. Rothman argues that “Philosophers find flaws or practical limitations in all philosophies of causal inference and the role of logic, belief, and observation in evaluating causal propositions is not settle yet.” However, even for a purely quantitative approach, our methods for resolving potential causes and measuring causal effect is limited in several ways.  Thus, a significant scientific undertaking, which would affect various disciplines, is to find better computational approaches to causal inference, especially in the face of complex and massive observational data.
*Judea Pearl

Contact us

YCIR is currently located in Pulmonary and Precision Medicine (P2Med) Data Hub at the 4th floor of Anlyan Center. Soon we will establish an online resource center and community environment to facilitate these activities. In the meantime, for discussions and sharing ideas, please Join our meetup group.

300 Cedar Street, New Haven, CT
Yale Causal Inference and Reasoning Working Group (YCIR)
Pulmonary and Precision Medicine (P2Med) Data Hub, 4th floor TAC building