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

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

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

A daily glass of wine prolongs life—yet alcohol can cause life-threatening cancer. Some say raising the minimum wage will decrease inequality while others say