scholarly journals 1070An online searchable database of example causal diagrams to make them easier to construct

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Tim Watkins
Keyword(s):  
Big Data ◽  
Cognitive Psychology ◽  
Cognitive Science ◽  
Directed Acyclic Graphs ◽  
Science Literature ◽  
Alternative Strategies ◽  
Searchable Database ◽  
Acyclic Graphs ◽  
Online Resource ◽  
Causal Diagrams

Abstract Focus of Presentation Most researchers do not use causal diagrams, in this case meaning directed acyclic graphs (DAGs), despite being widely recommended in epidemiology. They can help to identify the biases that might lead to faulty conclusions or suggest variables for which data should be collected and included in a model. Seeking to understand this reluctance and develop alternative strategies that might increase the use of causal diagrams, we searched the cognitive science literature for potential reasons and suggestions. Findings Insights from cognitive psychology led to a better understanding of the barriers that might underlie the reluctance to use causal diagrams. This includes our built-in desire for cognitive ease and suggests that strategies which lower the effort required to create a diagram may help. We explain these findings using example projects from neuropsychiatry big data research and describe how an online resource we have created has helped. Conclusions/Implications A causal diagram website has been created that aims to lower the effort needed to create a diagram for a study. It contains tutorials and a terminology guide, as well as links to other tutorials; a guide to software and other resources that might be used; and a searchable database of example causal diagrams with links to published articles that include them. Key messages A website has been developed to help overcome barriers to the use of causal diagrams. With contributions welcome.

scholarly journals Causal inference concepts applied to three observational studies in the context of vaccine development: from theory to practice

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Emilia Gvozdenović ◽  
Lucio Malvisi ◽  
Elisa Cinconze ◽  
Stijn Vansteelandt ◽  
Phoebe Nakanwagi ◽  
...  
Keyword(s):  
Cohort Study ◽  
Causal Inference ◽  
Observational Studies ◽  
Vaccine Development ◽  
Directed Acyclic Graphs ◽  
Counterfactual Reasoning ◽  
Confounding Bias ◽  
Accurate Design ◽  
Acyclic Graphs ◽  
Causal Diagrams

Abstract Background Randomized controlled trials are considered the gold standard to evaluate causal associations, whereas assessing causality in observational studies is challenging. Methods We applied Hill’s Criteria, counterfactual reasoning, and causal diagrams to evaluate a potentially causal relationship between an exposure and outcome in three published observational studies: a) one burden of disease cohort study to determine the association between type 2 diabetes and herpes zoster, b) one post-authorization safety cohort study to assess the effect of AS04-HPV-16/18 vaccine on the risk of autoimmune diseases, and c) one matched case-control study to evaluate the effectiveness of a rotavirus vaccine in preventing hospitalization for rotavirus gastroenteritis. Results Among the 9 Hill’s criteria, 8 (Strength, Consistency, Specificity, Temporality, Plausibility, Coherence, Analogy, Experiment) were considered as met for study c, 3 (Temporality, Plausibility, Coherence) for study a, and 2 (Temporary, Plausibility) for study b. For counterfactual reasoning criteria, exchangeability, the most critical assumption, could not be tested. Using these tools, we concluded that causality was very unlikely in study b, unlikely in study a, and very likely in study c. Directed acyclic graphs provided complementary visual structures that identified confounding bias and helped determine the most accurate design and analysis to assess causality. Conclusions Based on our assessment we found causal Hill’s criteria and counterfactual thinking valuable in determining some level of certainty about causality in observational studies. Application of causal inference frameworks should be considered in designing and interpreting observational studies.

scholarly journals An Introduction to Causal Diagrams for Anesthesiology Research

2020 ◽  
Vol 132 (5) ◽  
pp. 951-967 ◽  
Author(s):  
Amy L. Gaskell ◽  
Jamie W. Sleigh
Keyword(s):  
Directed Acyclic Graphs ◽  
Lessons Learned ◽  
Anesthetic Technique ◽  
Intravenous Anesthesia ◽  
Anesthesia Research ◽  
Randomized Controlled ◽  
Acyclic Graphs ◽  
Causal Diagrams ◽  
After Cancer ◽  
Inference Techniques

Abstract Making good decisions in the era of Big Data requires a sophisticated approach to causality. We are acutely aware that association ≠ causation, yet untangling the two remains one of our greatest challenges. This realization has stimulated a Causal Revolution in epidemiology, and the lessons learned are highly relevant to anesthesia research. This article introduces readers to directed acyclic graphs; a cornerstone of modern causal inference techniques. These diagrams provide a robust framework to address sources of bias and discover causal effects. We use the topical question of whether anesthetic technique (total intravenous anesthesia vs. volatile) affects outcome after cancer surgery as a basis for a series of example directed acyclic graphs, which demonstrate how variables can be chosen to statistically control confounding and other sources of bias. We also illustrate how controlling for the wrong variables can introduce, rather than eliminate, bias; and how directed acyclic graphs can help us diagnose this problem. This is a rapidly evolving field, and we cover only the most basic elements. The true promise of these techniques is that it may become possible to make robust statements about causation from observational studies—without the expense and artificiality of randomized controlled trials.

Causal Inference, Causal Effect Estimation, and Systematic Error

2019 ◽  
pp. 41-78
Author(s):  
Daniel Westreich
Keyword(s):  
Causal Inference ◽  
Systematic Error ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Directed Acyclic Graphs ◽  
Confounding Bias ◽  
Acyclic Graphs ◽  
Causal Diagrams ◽  
Alternative Approaches ◽  
Effect Estimation

Chapter 3 discusses basic concepts in causal inference, beginning with an introduction to potential outcomes and definitions of causal contrasts (or causal estimates of effect), concepts, terms, and notation. Many concepts introduced here will be developed further in subsequent chapters. The author discusses sufficient conditions for estimation of causal effects (which are sometimes called causal identification conditions), causal directed acyclic graphs (sometimes called causal diagrams), and four key types of systematic error (confounding bias, missing data bias, selection bias, and measurement error/information bias). The author also briefly discusses alternative approaches to causal inference.

scholarly journals Directed acyclic graphs: An under-utilized tool for child maltreatment research

2019 ◽  
Vol 91 ◽  
pp. 78-87 ◽  
Author(s):  
Anna E. Austin ◽  
Tania A. Desrosiers ◽  
Meghan E. Shanahan
Keyword(s):  
Child Maltreatment ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs

scholarly journals On directed analogues of expander and hyperfinite graph sequences

2021 ◽  
pp. 1-14
Author(s):  
Endre Csóka ◽  
Łukasz Grabowski
Keyword(s):  
Directed Acyclic Graphs ◽  
Acyclic Graphs ◽  
Probabilistic Construction

Abstract We introduce and study analogues of expander and hyperfinite graph sequences in the context of directed acyclic graphs, which we call ‘extender’ and ‘hypershallow’ graph sequences, respectively. Our main result is a probabilistic construction of non-hypershallow graph sequences.

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