This review presents empirical researchers with recent advances in causal inference, and
stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical …

This paper aims at assisting empirical researchers benefit from recent advances in causal
inference. The paper stresses the paradigmatic shifts that must be undertaken in moving …

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

This paper reviews a theory of causal inference based on the Structural Causal Model
(SCM) described in Pearl (2000a). The theory unifies the graphical, potential-outcome …

1. BACKGROUND The field of statistics has seen many well-meaning crusades against
threats from metaphysics and other heresy. In its founding prospectus of 1834, the Royal …

Simplifies the treatment of statistical inference focusing on how to specify and interpret
models in the context of testing causal theories. Simple bivariate regression, multiple …

The past decade has witnessed an explosion of interest in research and education in causal
inference, due to its wide applications in biomedical research, social sciences, artificial …

Myth, Confusion, and Science in Causal Analysis Page 1 STATISTICS IN MEDICINE Statist.
Med. 2000; 00:1–6 Prepared using simauth.cls [Version: 2002/09/18 v1.11] Myth, Confusion …

CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding
and use of data. Without an understanding of cause–effect relationships, we cannot use data …

This paper provides a conceptual introduction to causal inference, aimed to assist health
services researchers benefit from recent advances in this area. The paper stresses the …