Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Causal Inference in the Health Sciences: A Conceptual Introduction

Causal Inference in the Health Sciences: A Conceptual Introduction 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Health Services and Outcomes Research Methodology Springer Journals

Causal Inference in the Health Sciences: A Conceptual Introduction

Loading next page...
 
/lp/springer-journal/causal-inference-in-the-health-sciences-a-conceptual-introduction-5NHYDCt820

References (97)

Publisher
Springer Journals
Copyright
Copyright © 2001 by Kluwer Academic Publishers
Subject
Medicine & Public Health; Public Health; Statistics, general; Economics, general; Methodology of the Social Sciences; Health Administration
ISSN
1387-3741
eISSN
1572-9400
DOI
10.1023/A:1020315127304
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Health Services and Outcomes Research MethodologySpringer Journals

Published: Oct 10, 2004

There are no references for this article.