A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is

One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is

An accessible, contemporary introduction to the methods for determining cause and effect in the Social Sciences “Causation versus correlation has been the bas

This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical

CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we