Advancing causal inference methods, fostering collaborative research, and providing essential training to drive innovation across diverse fields.
Formal causal reasoning is essential at every research stage, from design to interpretation. The CCI develops study designs and analytic methods for various fields, with a focus on precision medicine. Unlike machine learning, which predicts outcomes if clinical practices remain unchanged, causal inference assesses potential outcomes under different interventions—identifying which treatments best suit specific patient groups. Through causal inference methods, the CCI transforms Big Data into actionable evidence for personalized medicine.
Discover the latest breakthroughs, insights, and events from the Center for Causal Inference.
Nandita Mitra, PhD, Professor of Biostatistics, has been awarded the 2024 L. Adrienne Cupples Award for her outstanding contributions to research, teaching, and leadership in biostatistics and public health.
We are pleased to announce that Bryan Blette and Andrew Ying were seclected as the winners of the highly competitive ASA Biometrics Section Paper award. The awardes will present their […]
Doubly robust nonparametric instrumental variable estimators for survival outcomes Youjin Lee, Edward H Kennedy, Nandita Mitra Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal […]
We are pleased to announce that Jason Roy, CCI Co-Director, has been selected for the 2021 ASA Causality in Statistics Education Award for his work to advance Causality in Statistics […]