Journal ArticleDOI
Causal diagrams for epidemiologic research.
Reads0
Chats0
TLDR
Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates and provide a method for critical evaluation of traditional epidemiologic criteria for confounding.Abstract:
Causal diagrams have a long history of informal use and, more recently, have undergone formal development for applications in expert systems and robotics. We provide an introduction to these developments and their use in epidemiologic research. Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates. They also provide a method for critical evaluation of traditional epidemiologic criteria for confounding. In particular, they reveal certain heretofore unnoticed shortcomings of those criteria when used in considering multiple potential confounders. We show how to modify the traditional criteria to correct those shortcomings.read more
Citations
More filters
Journal ArticleDOI
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
TL;DR: The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.
Journal ArticleDOI
Particulate Matter Air Pollution and Cardiovascular Disease An Update to the Scientific Statement From the American Heart Association
Robert D. Brook,Sanjay Rajagopalan,C. Arden Pope,Jeffrey R. Brook,Aruni Bhatnagar,Ana V. Diez-Roux,Fernando Holguin,Yuling Hong,Russell V. Luepker,Murray A. Mittleman,Annette Peters,David S. Siscovick,Sidney C. Smith,Laurie P. Whitsel,Joel D. Kaufman +14 more
TL;DR: It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM2.5 exposure and cardiovascular morbidity and mortality.
Journal ArticleDOI
Marginal Structural Models and Causal Inference in Epidemiology
TL;DR: In this paper, the authors introduce marginal structural models, a new class of causal models that allow for improved adjustment of confounding in observational studies with exposures or treatments that vary over time, when there exist time-dependent confounders that are also affected by previous treatment.
Journal ArticleDOI
An atlas of genetic correlations across human diseases and traits.
Brendan Bulik-Sullivan,Brendan Bulik-Sullivan,Hilary K. Finucane,Verneri Anttila,Verneri Anttila,Alexander Gusev,Felix R. Day,Po-Ru Loh,Po-Ru Loh,Laramie E. Duncan,Laramie E. Duncan,John R. B. Perry,Nick Patterson,Elise B. Robinson,Elise B. Robinson,Mark J. Daly,Mark J. Daly,Alkes L. Price,Alkes L. Price,Benjamin M. Neale,Benjamin M. Neale +20 more
TL;DR: This work introduces a technique—cross-trait LD Score regression—for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap, and uses this method to estimate 276 genetic correlations among 24 traits.
Journal ArticleDOI
Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies
TL;DR: A suite of quantitative and qualitative methods are described that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample to contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data.
References
More filters
Book
Causation, prediction, and search
TL;DR: The authors axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models.
Journal ArticleDOI
Correlation and Causation
Victor R. Martuza,David A. Kenny +1 more
TL;DR: Causality is the area of statistics that is most commonly misused, and misinterpreted, by nonspecialists as discussed by the authors, who fail to understand that, just because results show a correlation, there is no proof of an underlying causality.
Journal ArticleDOI
Causal diagrams for empirical research
TL;DR: In this paper, a nonparametric framework for causal inference is proposed, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data.
Journal ArticleDOI
Limitations of the application of fourfold table analysis to hospital data.
TL;DR: In this paper, the authors present a method for determining the effect of an agent or process that may be considered typical in the biologic laboratory, which consists in dividing a group of animals into two cohorts, one considered the experimental group, the other the control.
Book
Statistical Models in Epidemiology
David Clayton,Michael Hills +1 more
TL;DR: This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level.