Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to sign up for SAGE Journal Email Alerts today!

Sign In to gain access to subscriptions and/or personal tools.
Evaluation Review
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (9)
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Freedman, D. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Freedman, D. A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Statistical Models for Causation

What Inferential Leverage Do They Provide?

David A. Freedman

University of California, Berkeley

Experiments offer more reliable evidence on causation than observational studies, which is not to gainsay the contribution to knowledge from observation. Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with little value added by "sophisticated" models. This article discusses current models for causation, as applied to experimental and observational data. The intention-totreat principle and the effect of treatment on the treated will also be discussed. Flaws in perprotocol and treatment-received estimates will be demonstrated.

Key Words: causation • models • experiments • observational studies • intention-to-treat • per-protocol • treatment-received • instrumental variables

Evaluation Review, Vol. 30, No. 6, 691-713 (2006)
DOI: 10.1177/0193841X06293771
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?


This article has been cited by other articles:


Home page
 Eval RevHome page
S. Konstantopoulos
Incorporating Cost in Power Analysis for Three-Level Cluster-Randomized Designs
Eval Rev, August 1, 2009; 33(4): 335 - 357.
[Abstract] [PDF]

Home page
 Eval RevHome page
E. L. C. Merrall and S. M. Bird
A Statistical Perspective on the Design of Drug-Court Studies
Eval Rev, June 1, 2009; 33(3): 257 - 280.
[Abstract] [PDF]

Home page
 Eval RevHome page
B. J. McMorris, R. S. Petrie, R. F. Catalano, C. B. Fleming, K. P. Haggerty, and R. D. Abbott
Use of Web and In-Person Survey Modes to Gather Data From Young Adults on Sex and Drug Use: An Evaluation of Cost, Time, and Survey Error Based on a Randomized Mixed-Mode Design
Eval Rev, April 1, 2009; 33(2): 138 - 158.
[Abstract] [PDF]

Home page
 POLIT ANALHome page
T. Dunning
Model Specification in Instrumental-Variables Regression
Political Analysis, July 1, 2008; 16(3): 290 - 302.
[Abstract] [Full Text] [PDF]

Home page
 Eval RevHome page
E. M. Wolf and D. A. Wolf
Mixed Results in a Transitional Planning Program for Alternative School Students
Eval Rev, April 1, 2008; 32(2): 187 - 215.
[Abstract] [PDF]

Home page
 Eval RevHome page
P. K. Robins
Welfare Reform and Child Care: Evidence From 10 Experimental Welfare-to-Work Programs
Eval Rev, October 1, 2007; 31(5): 440 - 468.
[Abstract] [PDF]