Jump to ContentJump to Main Navigation
Show Summary Details

The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.


IMPACT FACTOR 2015: 0.667
5-year IMPACT FACTOR: 1.188

SCImago Journal Rank (SJR) 2015: 0.495
Source Normalized Impact per Paper (SNIP) 2015: 0.180
Impact per Publication (IPP) 2015: 0.319

Mathematical Citation Quotient (MCQ) 2015: 0.04

99,00 € / $149.00 / £75.00*

Online
ISSN
1557-4679
See all formats and pricing

 


Select Volume and Issue
Loading journal volume and issue information...

An Introduction to Causal Inference

Judea Pearl1

1University of California, Los Angeles

Citation Information: The International Journal of Biostatistics. Volume 6, Issue 2, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1203, February 2010

Publication History

Published Online:
2010-02-26

This paper summarizes recent advances in causal inference and underscores 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, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.

Keywords: structural equation models; confounding; graphical methods; counterfactuals; causal effects; potential-outcome; mediation; policy evaluation; causes of effects

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Huaizhen Qin, Nathan Morris, Sun J. Kang, Mingyao Li, Bamidele Tayo, Helen Lyon, Joel Hirschhorn, Richard S. Cooper, and Xiaofeng Zhu
Bioinformatics, 2010, Volume 26, Number 23, Page 2961
[2]
Jim Ridgway
International Statistical Review, 2015, Page n/a
[3]
Donald R. Schoolmaster, James B. Grace, E. William Schweiger, Brian R. Mitchell, and Glenn R. Guntenspergen
Ecological Indicators, 2013, Volume 29, Page 411
[5]
James Fackler, Harold P. Lehmann, and Randall C. Wetzel
Pediatric Critical Care Medicine, 2015, Volume 16, Number 3, Page 297
[6]
Myoung-jae Lee and Fali Huang
Journal of the Royal Statistical Society: Series A (Statistics in Society), 2012, Volume 175, Number 2, Page 535
[7]
Sander Greenland and Judea Pearl
International Statistical Review, 2011, Volume 79, Number 3, Page 401
[8]
Wayne Martin
Preventive Veterinary Medicine, 2014, Volume 113, Number 3, Page 281
[9]
J. Detilleux, J.P. Kastelic, and H.W. Barkema
Preventive Veterinary Medicine, 2015, Volume 118, Number 4, Page 449
[10]
Judea Pearl
Sociological Methodology, 2010, Volume 40, Number 1, Page 75
[11]
Romain Neugebauer, Julie A. Schmittdiel, Zheng Zhu, Jeremy A. Rassen, John D. Seeger, and Sebastian Schneeweiss
Statistics in Medicine, 2015, Volume 34, Number 5, Page 753
[12]
Shikui Tu, Thoru Pederson, and Zhiping Weng
Nucleus, 2013, Volume 4, Number 2, Page 89
[13]
Luis M Franco, Kristine L Bucasas, Janet M Wells, Diane Niño, Xueqing Wang, Gladys E Zapata, Nancy Arden, Alexander Renwick, Peng Yu, John M Quarles, Molly S Bray, Robert B Couch, John W Belmont, and Chad A Shaw
eLife, 2013, Volume 2
[14]
Alejandro Villaverde, John Ross, and Julio Banga
Cells, 2013, Volume 2, Number 2, Page 306
[15]
Molly Perencevich, Rohit P. Ojha, Ewout W. Steyerberg, and Sapna Syngal
Gastroenterology, 2013, Volume 145, Number 4, Page 775
[16]
Roger M. Harbord, Vanessa Didelez, Tom M. Palmer, Sha Meng, Jonathan A.C. Sterne, and Nuala A. Sheehan
Statistics in Medicine, 2013, Volume 32, Number 7, Page 1246
[17]
[18]
E. M. Jones, J. R. Thompson, V. Didelez, and N. A. Sheehan
Statistics in Medicine, 2012, Volume 31, Number 14, Page 1483
[19]
Benjamin P. Chapman, Brent Roberts, and Paul Duberstein
Journal of Aging Research, 2011, Volume 2011, Page 1

Comments (0)

Please log in or register to comment.