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Causal Inference

Objective: Questions of causality are fundamental inspirations behind innovations in philosophy, business, and science, including biomedicine. Answering causal questions leads to better predictive and prescriptive modeling. It is also the key to the correct identification of unknown effects as well as the latent factors that influence outcomes, and to produce hypotheses, validation, and proof.

Large-scale observational data offers a new window for verifying our existing causal understandings and for inferring new causal relationships at a fast pace. We are working to rethink the existing frameworks of causal inference in health sciences by introducing ideas from other scientific disciplines and by inventing new concepts and analytical methods.

  • Please add your name in the following if you are interested in joining.
  • If you have an ongoing project in causal inference in observational biomedical/ health data, please add the project name/link and a two-line description to the projects section
  • If you have an idea on causal inference that you want to test it or even develop it, you probably will find collaborators here

Project Lead(s):

* Abbas Shojaee


  • Alireza Aani
  • Yalda Aryan
  • Menelaos Konstantinidis

Ongoing Projects

  • Causal Inference Using Composition of Transactions



News Link


Literature Repository


projects/workgroups/causal_inference.1574376115.txt.gz · Last modified: 2019/11/21 22:41 by abbas