Causal Inference in the Health Sciences: A Conceptual Introduction
 Judea Pearl
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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.
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 Title
 Causal Inference in the Health Sciences: A Conceptual Introduction
 Journal

Health Services and Outcomes Research Methodology
Volume 2, Issue 34 , pp 189220
 Cover Date
 20011201
 DOI
 10.1023/A:1020315127304
 Print ISSN
 13873741
 Online ISSN
 15729400
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 structural equation models
 confounding
 noncompliance
 graphical methods
 counterfactuals
 Industry Sectors
 Authors

 Judea Pearl ^{(1)}
 Author Affiliations

 1. Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA, 90024