Statistical tools for valid causal inference with fewer assumptions

The original article can be found here.

Causal inference is important in medical research to help determine if treatments are beneficial and if natural exposures are harmful. In many settings, data collection makes causal inference difficult without making overly optimistic or idealistic assumptions. In a new article published in the Journal of the American Statistical Association, researchers at Karolinska Institutet develop new statistical methods to make causal inference possible in some settings without making such assumptions.

The authors Erin Gabriel, Michael Sachs and Arvid Sjölander at the Department of Medical Epidemiology and Biostatistics, describe in the new paper how these methods can be used and interpreted.

“These statistical methods, which are easy to implement, may help in many settings where causal inference is threatened by unmeasured confounding and/or selection bias,” says first author Erin Gabriel.

The authors hope that their tools will be used by researchers around the world to help them make decisions without having to guess about unmeasured factors in their data. In their ongoing and future work, they aim to build and describe new statistical tools that can be used in imperfect clinical trials.

In the field of Causal Inference, Professor Judea Pearl is a pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning.”  In 2020, Michael Dukakis Institute also awarded Professor Pearl as World Leader in AI World Society (AIWS.net) for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea also contributes to Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.