Science News

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.
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