Health Services and Outcomes Research Methodology

, Volume 2, Issue 3, pp 189-220

First online:

Causal Inference in the Health Sciences: A Conceptual Introduction

  • Judea PearlAffiliated withCognitive Systems Laboratory, Computer Science Department, University of California

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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 paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, corrections for noncompliance, and a symbiosis between counterfactual and graphical methods of analysis.

structural equation models confounding noncompliance graphical methods counterfactuals