Causal Analysis in Biomedicine and Epidemiology: Based on Minimal Sufficient Causation

237 Pages·2001·9.619 MB·\237

by  Mikel Aickin (Author)

CAUSAL ANALYSIS
IN BIOMEDICINE
AND EPIDEMIOLOGY
Biostatistics: A Series of References and Textbooks
Series Editor
Shein-Chung Chow
President, U.S. Operations
StatPlus, Inc.
Yandley, Pennsylvania
Adjunct Professor
Temple University
Philadelphia, Pennsylvania
1. Design and Analysis of Animal Studies in Pharmaceutical Devel-
opment, edited by Shein-Chung Chow and Jen-pei Liu
2. Basic Statistics and Pharmaceutical Statistical Applications, James E.
De Muth
3. Design and Analysis of Bioavailability and Bioequh/alence Studies,
Second Edition, Revised and Expanded, Shein-Chung Chow and
Jen-pei Liu
4. Meta-Analysis in Medicine and Health Policy, edited by Dalene K.
Stangl and Donald A. Berry
5. Generalized Linear Models: A Bayesian Perspective, edited by Dipak
K. Dey, Sujit K. Ghosh, and Bani K. Mallick
6. Difference Equations with Public Health Applications, Lemuel A. Moy6
and Asha Seth Kapadia
7. Medical Biostatistics, Abhaya Indrayan and Sanjeev B. Sarmukaddam
8. Statistical Methods for Clinical Trials, Mark X. Norleans
9. Causal Analysis in Biomedicine and Epidemiology: Based on Minimal
Sufficient Causation, Mikel Aickin
ADDITIONAL VOLUMES IN PREPARATION
CAUSAL ANALYSIS
IN BIOMEDICINE
AND EPIDEMIOLOGY
BASED ON MINIMAL
SUFFICIENT CAUSATION
MIKEL AICKIN
Center for Health Research
Kaiser Permanente Northwest Division
Portland, Oregon
MARCEL DEKKER, INC. NEW YORK • BASEL
ISBN: 0-8247-0748-6
This book is printed on acid-free paper.
Headquarters
Marcel Dekker, Inc.
270 Madison Avenue, New York, NY 10016
tel: [phone]; fax: [phone]
Eastern Hemisphere Distribution
Marcel Dekker AG
Hutgasse 4, Postfach 812, CH-4001 Basel, Switzerland
tel: [phone]; fax: [phone]
World Wide Web
http://www.dekker.com
The publisher offers discounts on this book when ordered in bulk quantities. For more infor-
mation, write to Special Sales/Professional Marketing at the headquarters address above.
Copyright © 2002 by Marcel Dekker, Inc. All Rights Reserved.
Neither this book nor any part may be reproduced or transmitted in any form or by any
means, electronic or mechanical, including photocopying, microfilming, and recording, or
by any information storage and retrieval system, without permission in writing from the
publisher.
Current printing (last digit):
10 9 8 7 6 5 4 3 21
PRINTED IN THE UNITED STATES OF AMERICA
ToCR

Preface
This is a book about the properties of causation. It is based on the
idea that even if we do not have a perfect definition of cause, we can
nonetheless describe some of its features, and that this will help us to
understand its nature. One of the miracles of the human mind is that
it can discuss and consider before it understands, and this seems to be
where we are at the present time regarding notions of causation.
The book is also about ways in which an understanding of
causation can affect how we think about biology, medicine, public
health, and epidemiology. As a practicing biostatistician I am all too
aware of the degree to which causation is not used in the biomedical
sciences, and I am reasonably sure that we will progress more rapidly
if we overcome our Humean phobia that causation may not be real.
I will admit that there is some mathematics here (mostly a new
variant of algebra), but I contend that it is simple, and like most
useful mathematics, gives back more in understanding than it extracts
in mental sweat. There is some philosophy here, but since I am not a
philosopher it is relatively straightforward and blessedly brief. There
is also some biomedical science here, either by example or by
indirection, because causation is at base a scientific topic. But given
all this, I have tried to write this book so that it transcends
disciplinary boundaries and speaks to the need we all have to make
causation an integral part of our thinking about science, and a natural
part of the way we talk about science.
M ikel Aickin
V

Contents
Preface v
Special Symbols and Phrases ix
1. Orientation 1
2. What Is Causation? 7
3. Naive Minimal Sufficient Cause 13
4. Events and Probabilities 21
5. Unitary Algebra 31
6. Nontrivial Implication 39
7. Tiny Examples 45
8. The One-Factor Model 49
9. Graphical Elements 59
10. Causations 67
11. Structural Equations 73
12. The Two-Factor Model 77
13. Down Syndrome Example 83
14. Marginalization 87
15. Stratification 91
16. Obesity Example 95
17. Attribution 101
18. Indirect Cause: Probabilities 107
vii