Epidemiology, Risk and Causation Conceptual and Methodological Issues in Public Health Science Alex Broadbent Background
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The Practice of Causal Inference in Cancer Epidemiology
Vol. 5. 303-31 1, April 1996 Cancer Epidemiology, Biomarkers & Prevention 303 Review The Practice of Causal Inference in Cancer Epidemiology Douglas L. Weedt and Lester S. Gorelic causes lung cancer (3) and to guide causal inference for occu- Preventive Oncology Branch ID. L. W.l and Comprehensive Minority pational and environmental diseases (4). From 1965 to 1995, Biomedical Program IL. S. 0.1. National Cancer Institute, Bethesda, Maryland many associations have been examined in terms of the central 20892 questions of causal inference. Causal inference is often practiced in review articles and editorials. There, epidemiologists (and others) summarize evi- Abstract dence and consider the issues of causality and public health Causal inference is an important link between the recommendations for specific exposure-cancer associations. practice of cancer epidemiology and effective cancer The purpose of this paper is to take a first step toward system- prevention. Although many papers and epidemiology atically reviewing the practice of causal inference in cancer textbooks have vigorously debated theoretical issues in epidemiology. Techniques used to assess causation and to make causal inference, almost no attention has been paid to the public health recommendations are summarized for two asso- issue of how causal inference is practiced. In this paper, ciations: alcohol and breast cancer, and vasectomy and prostate we review two series of review papers published between cancer. The alcohol and breast cancer association is timely, 1985 and 1994 to find answers to the following questions: controversial, and in the public eye (5). It involves a common which studies and prior review papers were cited, which exposure and a common cancer and has a large body of em- causal criteria were used, and what causal conclusions pirical evidence; over 50 studies and over a dozen reviews have and public health recommendations ensued. -
Statistics and Causal Inference (With Discussion)
Applied Statistics Lecture Notes Kosuke Imai Department of Politics Princeton University February 2, 2008 Making statistical inferences means to learn about what you do not observe, which is called parameters, from what you do observe, which is called data. We learn the basic principles of statistical inference from a perspective of causal inference, which is a popular goal of political science research. Namely, we study statistics by learning how to make causal inferences with statistical methods. 1 Statistical Framework of Causal Inference What do we exactly mean when we say “An event A causes another event B”? Whether explicitly or implicitly, this question is asked and answered all the time in political science research. The most commonly used statistical framework of causality is based on the notion of counterfactuals (see Holland, 1986). That is, we ask the question “What would have happened if an event A were absent (or existent)?” The following example illustrates the fact that some causal questions are more difficult to answer than others. Example 1 (Counterfactual and Causality) Interpret each of the following statements as a causal statement. 1. A politician voted for the education bill because she is a democrat. 2. A politician voted for the education bill because she is liberal. 3. A politician voted for the education bill because she is a woman. In this framework, therefore, the fundamental problem of causal inference is that the coun- terfactual outcomes cannot be observed, and yet any causal inference requires both factual and counterfactual outcomes. This idea is formalized below using the potential outcomes notation. -
Bayesian Causal Inference
Bayesian Causal Inference Maximilian Kurthen Master’s Thesis Max Planck Institute for Astrophysics Within the Elite Master Program Theoretical and Mathematical Physics Ludwig Maximilian University of Munich Technical University of Munich Supervisor: PD Dr. Torsten Enßlin Munich, September 12, 2018 Abstract In this thesis we address the problem of two-variable causal inference. This task refers to inferring an existing causal relation between two random variables (i.e. X → Y or Y → X ) from purely observational data. We begin by outlining a few basic definitions in the context of causal discovery, following the widely used do-Calculus [Pea00]. We continue by briefly reviewing a number of state-of-the-art methods, including very recent ones such as CGNN [Gou+17] and KCDC [MST18]. The main contribution is the introduction of a novel inference model where we assume a Bayesian hierarchical model, pursuing the strategy of Bayesian model selection. In our model the distribution of the cause variable is given by a Poisson lognormal distribution, which allows to explicitly regard discretization effects. We assume Fourier diagonal covariance operators, where the values on the diagonal are given by power spectra. In the most shallow model these power spectra and the noise variance are fixed hyperparameters. In a deeper inference model we replace the noise variance as a given prior by expanding the inference over the noise variance itself, assuming only a smooth spatial structure of the noise variance. Finally, we make a similar expansion for the power spectra, replacing fixed power spectra as hyperparameters by an inference over those, where again smoothness enforcing priors are assumed. -
(STROBE) Statement: Guidelines for Reporting Observational Studies
Policy and practice The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies* Erik von Elm,a Douglas G Altman,b Matthias Egger,a,c Stuart J Pocock,d Peter C Gøtzsche e & Jan P Vandenbroucke f for the STROBE Initiative Abstract Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study’s generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control and cross-sectional studies. We convened a two-day workshop, in September 2004, with methodologists, researchers and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies. -
Articles Causal Inference in Civil Rights Litigation
VOLUME 122 DECEMBER 2008 NUMBER 2 © 2008 by The Harvard Law Review Association ARTICLES CAUSAL INFERENCE IN CIVIL RIGHTS LITIGATION D. James Greiner TABLE OF CONTENTS I. REGRESSION’S DIFFICULTIES AND THE ABSENCE OF A CAUSAL INFERENCE FRAMEWORK ...................................................540 A. What Is Regression? .........................................................................................................540 B. Problems with Regression: Bias, Ill-Posed Questions, and Specification Issues .....543 1. Bias of the Analyst......................................................................................................544 2. Ill-Posed Questions......................................................................................................544 3. Nature of the Model ...................................................................................................545 4. One Regression, or Two?............................................................................................555 C. The Fundamental Problem: Absence of a Causal Framework ....................................556 II. POTENTIAL OUTCOMES: DEFINING CAUSAL EFFECTS AND BEYOND .................557 A. Primitive Concepts: Treatment, Units, and the Fundamental Problem.....................558 B. Additional Units and the Non-Interference Assumption.............................................560 C. Donating Values: Filling in the Missing Counterfactuals ...........................................562 D. Randomization: Balance in Background Variables ......................................................563 -
Using Epidemiological Evidence in Tort Law: a Practical Guide Aleksandra Kobyasheva
Using epidemiological evidence in tort law: a practical guide Aleksandra Kobyasheva Introduction Epidemiology is the study of disease patterns in populations which seeks to identify and understand causes of disease. By using this data to predict how and when diseases are likely to arise, it aims to prevent the disease and its consequences through public health regulation or the development of medication. But can this data also be used to tell us something about an event that has already occurred? To illustrate, consider a case where a claimant is exposed to a substance due to the defendant’s negligence and subsequently contracts a disease. There is not enough evidence available to satisfy the traditional ‘but-for’ test of causation.1 However, an epidemiological study shows that there is a potential causal link between the substance and the disease. What is, or what should be, the significance of this study? In Sienkiewicz v Greif members of the Supreme Court were sceptical that epidemiological evidence can have a useful role to play in determining questions of causation.2 The issue in that case was a narrow one that did not present the full range of concerns raised by epidemiological evidence, but the court’s general attitude was not unusual. Few English courts have thought seriously about the ways in which epidemiological evidence should be approached or interpreted; in particular, the factors which should be taken into account when applying the ‘balance of probability’ test, or the weight which should be attached to such factors. As a result, this article aims to explore this question from a practical perspective. -
Analysis of Scientific Research on Eyewitness Identification
ANALYSIS OF SCIENTIFIC RESEARCH ON EYEWITNESS IDENTIFICATION January 2018 Patricia A. Riley U.S. Attorney’s Office, DC Table of Contents RESEARCH DOES NOT PROVIDE A FIRM FOUNDATION FOR JURY INSTRUCTIONS OF THE TYPE ADOPTED BY SOME COURTS OR, IN SOME INSTANCES, FOR EXPERT TESTIMONY ...................................................... 6 RESEARCH DOES NOT PROVIDE A FIRM FOUNDATION FOR JURY INSTRUCTIONS OF THE TYPE ADOPTED BY SOME COURTS AND, IN SOME INSTANCES, FOR EXPERT TESTIMONY .................................................... 8 Introduction ............................................................................................................................................. 8 Courts should not comment on the evidence or take judicial notice of contested facts ................... 11 Jury Instructions based on flawed or outdated research should not be given .................................. 12 AN OVERVIEW OF THE RESEARCH ON EYEWITNESS IDENTIFICATION OF STRANGERS, NEW AND OLD .... 16 Important Points .................................................................................................................................... 16 System Variables .................................................................................................................................... 16 Simultaneous versus sequential presentation ................................................................................... 16 Double blind, blind, blinded administration (unless impracticable ................................................... -
STATISTICAL SCIENCE Volume 36, Number 3 August 2021
STATISTICAL SCIENCE Volume 36, Number 3 August 2021 Khinchin’s 1929 Paper on Von Mises’ Frequency Theory of Probability . Lukas M. Verburgt 339 A Problem in Forensic Science Highlighting the Differences between the Bayes Factor and LikelihoodRatio...........................Danica M. Ommen and Christopher P. Saunders 344 A Horse Race between the Block Maxima Method and the Peak–over–Threshold Approach ..................................................................Axel Bücher and Chen Zhou 360 A Hybrid Scan Gibbs Sampler for Bayesian Models with Latent Variables ..........................Grant Backlund, James P. Hobert, Yeun Ji Jung and Kshitij Khare 379 Maximum Likelihood Multiple Imputation: Faster Imputations and Consistent Standard ErrorsWithoutPosteriorDraws...............Paul T. von Hippel and Jonathan W. Bartlett 400 RandomMatrixTheoryandItsApplications.............................Alan Julian Izenman 421 The GENIUS Approach to Robust Mendelian Randomization Inference .....................................Eric Tchetgen Tchetgen, BaoLuo Sun and Stefan Walter 443 AGeneralFrameworkfortheAnalysisofAdaptiveExperiments............Ian C. Marschner 465 Statistical Science [ISSN 0883-4237 (print); ISSN 2168-8745 (online)], Volume 36, Number 3, August 2021. Published quarterly by the Institute of Mathematical Statistics, 9760 Smith Road, Waite Hill, Ohio 44094, USA. Periodicals postage paid at Cleveland, Ohio and at additional mailing offices. POSTMASTER: Send address changes to Statistical Science, Institute of Mathematical Statistics, Dues and Subscriptions Office, PO Box 729, Middletown, Maryland 21769, USA. Copyright © 2021 by the Institute of Mathematical Statistics Printed in the United States of America Statistical Science Volume 36, Number 3 (339–492) August 2021 Volume 36 Number 3 August 2021 Khinchin’s 1929 Paper on Von Mises’ Frequency Theory of Probability Lukas M. Verburgt A Problem in Forensic Science Highlighting the Differences between the Bayes Factor and Likelihood Ratio Danica M. -
A Matching Based Theoretical Framework for Estimating Probability of Causation
A Matching Based Theoretical Framework for Estimating Probability of Causation Tapajit Dey and Audris Mockus Department of Electrical Engineering and Computer Science University of Tennessee, Knoxville Abstract The concept of Probability of Causation (PC) is critically important in legal contexts and can help in many other domains. While it has been around since 1986, current operationalizations can obtain only the minimum and maximum values of PC, and do not apply for purely observational data. We present a theoretical framework to estimate the distribution of PC from experimental and from purely observational data. We illustrate additional problems of the existing operationalizations and show how our method can be used to address them. We also provide two illustrative examples of how our method is used and how factors like sample size or rarity of events can influence the distribution of PC. We hope this will make the concept of PC more widely usable in practice. Keywords: Probability of Causation, Causality, Cause of Effect, Matching 1. Introduction Our understanding of the world comes from our knowledge about the cause-effect relationship between various events. However, in most of the real world scenarios, one event is caused by multiple other events, having arXiv:1808.04139v1 [stat.ME] 13 Aug 2018 varied degrees of influence over the first event. There are two major concepts associated with such relationships: Cause of Effect (CoE) and Effect of Cause (EoC) [1, 2]. EoC focuses on the question that if event X is the cause/one of the causes of event Y, then what is the probability of event Y given event Email address: [email protected], [email protected] (Tapajit Dey and Audris Mockus) Preprint submitted to arXiv July 2, 2019 X is observed? CoE, on the other hand, tries to answer the question that if event X is known to be (one of) the cause(s) of event Y, then given we have observed both events X and Y, what is the probability that event X was in fact the cause of event Y? In this paper, we focus on the CoE scenario. -
Dictionary Epidemiology
A Dictionary ofof Epidemiology Fifth Edition Edited for the International Epidemiological Association by Miquel Porta Professor of Preventive Medicine & Public Health, School of Medicine, Universitat Autònoma de Barcelona; Senior Scientist, Institut Municipal d’Investigació Mèdica, Barcelona, Spain; Adjunct Professor of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill Associate Editors Sander Greenland John M. Last 1 2008 PPorta_FM.inddorta_FM.indd iiiiii 44/16/08/16/08 111:51:201:51:20 PPMM 1 Oxford University Press, Inc., publishes works that further Oxford University’s objective excellence in research, schollarship, and education Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi Shanghai Taipei Toronto With offi ces in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Copyright © 1983, 1988, 1995, 2001, 2008 International Epidemiological Association, Inc. Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 www.oup-usa.org All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. This edition was prepared with support from Esteve Foundation (Barcelona, Catalonia, Spain) (http://www.esteve.org) Library of Congress Cataloging-in-Publication Data A dictionary of epidemiology / edited for the International Epidemiological Association by Miquel Porta; associate editors, John M. Last . [et al.].—5th ed. p. cm. Includes bibliographical references and index. -
Causation and Causal Inference in Epidemiology
PUBLIC HEALTH MAnERS Causation and Causal Inference in Epidemiology Kenneth J. Rothman. DrPH. Sander Greenland. MA, MS, DrPH. C Stat fixed. In other words, a cause of a disease Concepts of cause and causal inference are largely self-taught from early learn- ing experiences. A model of causation that describes causes in terms of suffi- event is an event, condition, or characteristic cient causes and their component causes illuminates important principles such that preceded the disease event and without as multicausality, the dependence of the strength of component causes on the which the disease event either would not prevalence of complementary component causes, and interaction between com- have occun-ed at all or would not have oc- ponent causes. curred until some later time. Under this defi- Philosophers agree that causal propositions cannot be proved, and find flaws or nition it may be that no specific event, condi- practical limitations in all philosophies of causal inference. Hence, the role of logic, tion, or characteristic is sufiicient by itself to belief, and observation in evaluating causal propositions is not settled. Causal produce disease. This is not a definition, then, inference in epidemiology is better viewed as an exercise in measurement of an of a complete causal mechanism, but only a effect rather than as a criterion-guided process for deciding whether an effect is pres- component of it. A "sufficient cause," which ent or not. (4m JPub//cHea/f/i. 2005;95:S144-S150. doi:10.2105/AJPH.2004.059204) means a complete causal mechanism, can be defined as a set of minimal conditions and What do we mean by causation? Even among eral. -
Probability, Explanation, and Inference: a Reply
Alabama Law Scholarly Commons Working Papers Faculty Scholarship 3-2-2017 Probability, Explanation, and Inference: A Reply Michael S. Pardo University of Alabama - School of Law, [email protected] Ronald J. Allen Northwestern University - Pritzker School of Law, [email protected] Follow this and additional works at: https://scholarship.law.ua.edu/fac_working_papers Recommended Citation Michael S. Pardo & Ronald J. Allen, Probability, Explanation, and Inference: A Reply, (2017). Available at: https://scholarship.law.ua.edu/fac_working_papers/4 This Working Paper is brought to you for free and open access by the Faculty Scholarship at Alabama Law Scholarly Commons. It has been accepted for inclusion in Working Papers by an authorized administrator of Alabama Law Scholarly Commons. Probability, Explanation, and Inference: A Reply Ronald J. Allen Michael S. Pardo 11 INTERNATIONAL JOURNAL OF EVIDENCE AND PROOF 307 (2007) This paper can be downloaded without charge from the Social Science Research Network Electronic Paper Collection: http://ssrn.com/abstract=2925866 Electronic copy available at: https://ssrn.com/abstract=2925866 PROBABILITY, EXPLANATION AND INFERENCE: A REPLY Probability, explanation and inference: a reply By Ronald J. Allen* and Wigmore Professor of Law, Northwestern University; Fellow, Procedural Law Research Center, China Political Science and Law University Michael S. Pardo† Assistant Professor, University of Alabama School of Law he inferences drawn from legal evidence may be understood in both probabilistic and explanatory terms. Consider evidence that a criminal T defendant confessed while in police custody. To evaluate the strength of this evidence in supporting the conclusion that the defendant is guilty, one could try to assess the probability that guilty and innocent persons confess while in police custody.