scholarly journals Introducing causal inference to the medical curriculum using temporal logic to draw directed acyclic graphs

2020 ◽  
Author(s):  
George TH Ellison
Keyword(s):  
Causal Inference ◽  
Critical Realism ◽  
Medical Curriculum ◽  
Statistical Modelling ◽  
Directed Acyclic Graphs ◽  
Unexpected Result ◽  
Technical Expertise ◽  
Acyclic Graphs ◽  
Medical Undergraduates ◽  
Directed Learning

Directed acyclic graphs (DAGs) might yet transform the statistical modelling of observational data for causal inference. This is because they offer a principled approach to analytical design that draws on existing contextual, empirical and theoretical knowledge, but ultimately relies on temporality alone to objectively specify probabilistic causal relationships amongst measured (and unmeasured) covariates, and the associated exposure and outcome variables. While a working knowledge of phenomenology, critical realism and epistemology seem likely to be useful for mastering the application of DAGs, drawing a DAG appears to require limited technical expertise and might therefore be accessible to even inexperienced and novice analysts. The present study evaluated the inclusion of a novel four-task directed learning exercise for medical undergraduates, which culminated in temporality-driven covariate classification, followed by DAG specification itself. The exercise achieved high levels of student engagement, although the proportion of students completing each of the exercise's four key tasks declined from close to 100% in tasks 1 and 2 (exposure and outcome specification; and covariate selection) to 83.5% and 77.6% in the third and fourth tasks, respectively. Fewer than 15% of the students successfully classified all of their covariates (as confounders, mediators or competing exposures) using temporality-driven classification, but this improved to more than 35% following DAG specification - an unexpected result given that all of the DAGs displayed at least one substantive technical error. These findings suggest that drawing a DAG, in and of itself, increases the utility of temporality-driven covariate classification for causal inference analysis; although further research is required to better understand: why even poorly specified DAGs might reduce covariate misclassification; how 'wrong but useful' DAGs might be identified; and how these marginal benefits might be enhanced with or without improvements in DAG specification.

Approaches to Improve Causal Inference in Physical Activity Epidemiology

2020 ◽  
Vol 17 (1) ◽  
pp. 80-84
Author(s):  
Brigid M. Lynch ◽  
Suzanne C. Dixon-Suen ◽  
Andrea Ramirez Varela ◽  
Yi Yang ◽  
Dallas R. English ◽  
...  
Keyword(s):  
Physical Activity ◽  
Causal Inference ◽  
Propensity Scores ◽  
Directed Acyclic Graphs ◽  
Epidemiological Methods ◽  
Bias Analysis ◽  
Randomized Controlled ◽  
Acyclic Graphs ◽  
Quantitative Bias Analysis ◽  
The Impact

Background: It is not always clear whether physical activity is causally related to health outcomes, or whether the associations are induced through confounding or other biases. Randomized controlled trials of physical activity are not feasible when outcomes of interest are rare or develop over many years. Thus, we need methods to improve causal inference in observational physical activity studies. Methods: We outline a range of approaches that can improve causal inference in observational physical activity research, and also discuss the impact of measurement error on results and methods to minimize this. Results: Key concepts and methods described include directed acyclic graphs, quantitative bias analysis, Mendelian randomization, and potential outcomes approaches which include propensity scores, g methods, and causal mediation. Conclusions: We provide a brief overview of some contemporary epidemiological methods that are beginning to be used in physical activity research. Adoption of these methods will help build a stronger body of evidence for the health benefits of physical activity.

scholarly journals Causal inference in cumulative risk assessment: The roles of directed acyclic graphs

2017 ◽  
Vol 102 ◽  
pp. 30-41 ◽  
Author(s):  
L. Elizabeth Brewer ◽  
J. Michael Wright ◽  
Glenn Rice ◽  
Lucas Neas ◽  
Linda Teuschler
Keyword(s):  
Risk Assessment ◽  
Causal Inference ◽  
Cumulative Risk ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs

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 Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs

2019 ◽  
Vol 49 (1) ◽  
pp. 322-329 ◽  
Author(s):  
Karl D Ferguson ◽  
Mark McCann ◽  
Srinivasa Vittal Katikireddi ◽  
Hilary Thomson ◽  
Michael J Green ◽  
...  
Keyword(s):  
Causal Inference ◽  
Modular Design ◽  
Evidence Synthesis ◽  
Empirical Studies ◽  
Directed Acyclic Graphs ◽  
Primary Data ◽  
Acyclic Graphs ◽  
Primary Data Analysis ◽  
Limited Experience ◽  
Direct Primary

Abstract Background Directed acyclic graphs (DAGs) are popular tools for identifying appropriate adjustment strategies for epidemiological analysis. However, a lack of direction on how to build them is problematic. As a solution, we propose using a combination of evidence synthesis strategies and causal inference principles to integrate the DAG-building exercise within the review stages of research projects. We demonstrate this idea by introducing a novel protocol: ‘Evidence Synthesis for Constructing Directed Acyclic Graphs’ (ESC-DAGs)’. Methods ESC-DAGs operates on empirical studies identified by a literature search, ideally a novel systematic review or review of systematic reviews. It involves three key stages: (i) the conclusions of each study are ‘mapped’ into a DAG; (ii) the causal structures in these DAGs are systematically assessed using several causal inference principles and are corrected accordingly; (iii) the resulting DAGs are then synthesised into one or more ‘integrated DAGs’. This demonstration article didactically applies ESC-DAGs to the literature on parental influences on offspring alcohol use during adolescence. Conclusions ESC-DAGs is a practical, systematic and transparent approach for developing DAGs from background knowledge. These DAGs can then direct primary data analysis and DAG-based sensitivity analysis. ESC-DAGs has a modular design to allow researchers who are experienced DAG users to both use and improve upon the approach. It is also accessible to researchers with limited experience of DAGs or evidence synthesis.

scholarly journals A biologist's guide to model selection and causal inference

2021 ◽  
Vol 288 (1943) ◽  
pp. 20202815
Author(s):  
Zachary M. Laubach ◽  
Eleanor J. Murray ◽  
Kim L. Hoke ◽  
Rebecca J. Safran ◽  
Wei Perng
Keyword(s):  
Data Analysis ◽  
Causal Inference ◽  
Analytical Framework ◽  
Directed Acyclic Graphs ◽  
Biological Processes ◽  
Large Complex ◽  
Acyclic Graphs ◽  
Research Programmes ◽  
And Behavior

A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data.

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 An Introduction to Causal Inference

2020 ◽  
Author(s):  
Fabian Dablander
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Directed Acyclic Graphs ◽  
Causal Models ◽  
Causal Relationships ◽  
Counterfactual Reasoning ◽  
Graphical Approach ◽  
Acyclic Graphs ◽  
Definition Of ◽  
Simpson's Paradox

Causal inference goes beyond prediction by modeling the outcome of interventions and formalizing counterfactual reasoning. Instead of restricting causal conclusions to experiments, causal inference explicates the conditions under which it is possible to draw causal conclusions even from observational data. In this paper, I provide a concise introduction to the graphical approach to causal inference, which uses Directed Acyclic Graphs (DAGs) to visualize, and Structural Causal Models (SCMs) to relate probabilistic and causal relationships. Successively, we climb what Judea Pearl calls the "causal hierarchy" --- moving from association to intervention to counterfactuals. I explain how DAGs can help us reason about associations between variables as well as interventions; how the do-calculus leads to a satisfactory definition of confounding, thereby clarifying, among other things, Simpson's paradox; and how SCMs enable us to reason about what could have been. Lastly, I discuss a number of challenges in applying causal inference in practice.

scholarly journals Robust causal inference using directed acyclic graphs: the R package ‘dagitty’

2017 ◽  
pp. dyw341 ◽  
Author(s):  
Johannes Textor ◽  
Benito van der Zander ◽  
Mark S. Gilthorpe ◽  
Maciej Liśkiewicz ◽  
George T.H. Ellison
Keyword(s):  
Causal Inference ◽  
R Package ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs

scholarly journals Decision-theoretic foundations for statistical causality

2021 ◽  
Vol 9 (1) ◽  
pp. 39-77
Author(s):  
Philip Dawid
Keyword(s):  
Decision Making ◽  
Causal Inference ◽  
Decision Problem ◽  
Directed Acyclic Graphs ◽  
Intention To Treat ◽  
External Data ◽  
Acyclic Graphs ◽  
Context Dependent ◽  
Theoretic Foundations

Abstract We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic (DT) statistical causality, which is a straightforward way of representing and addressing causal questions. DT reframes causal inference as “assisted decision-making” and aims to understand when, and how, I can make use of external data, typically observational, to help me solve a decision problem by taking advantage of assumed relationships between the data and my problem. The relationships embodied in any representation of a causal problem require deeper justification, which is necessarily context-dependent. Here we clarify the considerations needed to support applications of the DT methodology. Exchangeability considerations are used to structure the required relationships, and a distinction drawn between intention to treat and intervention to treat forms the basis for the enabling condition of “ignorability.” We also show how the DT perspective unifies and sheds light on other popular formalisations of statistical causality, including potential responses and directed acyclic graphs.

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