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.

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 Compartmental Model Diagrams as Causal Representations in Relation to DAGs

2017 ◽  
Vol 6 (1) ◽  
Author(s):  
Sarah F. Ackley ◽  
Elizabeth Rose Mayeda ◽  
Lee Worden ◽  
Wayne T. A. Enanoria ◽  
M. Maria Glymour ◽  
...  
Keyword(s):  
Compartmental Model ◽  
Directed Acyclic Graphs ◽  
Sensitivity Analyses ◽  
Disease Epidemiology ◽  
Infectious Disease Epidemiology ◽  
Causal Processes ◽  
Acyclic Graphs ◽  
Dementia Incidence

AbstractCompartmental model diagrams have been used for nearly a century to depict causal relationships in infectious disease epidemiology. Causal directed acyclic graphs (DAGs) have been used more broadly in epidemiology since the 1990s to guide analyses of a variety of public health problems. Using an example from chronic disease epidemiology, the effect of type 2 diabetes on dementia incidence, we illustrate how compartmental model diagrams can represent the same concepts as causal DAGs, including causation, mediation, confounding, and collider bias. We show how to use compartmental model diagrams to explicitly depict interaction and feedback cycles. While DAGs imply a set of conditional independencies, they do not define conditional distributions parametrically. Compartmental model diagrams parametrically (or semiparametrically) describe state changes based on known biological processes or mechanisms. Compartmental model diagrams are part of a long-term tradition of causal thinking in epidemiology and can parametrically express the same concepts as DAGs, as well as explicitly depict feedback cycles and interactions. As causal inference efforts in epidemiology increasingly draw on simulations and quantitative sensitivity analyses, compartmental model diagrams may be of use to a wider audience. Recognizing simple links between these two common approaches to representing causal processes may facilitate communication between researchers from different traditions.

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 The OptoGenBox - a device for long-term optogenetics in C. elegans

2020 ◽  
Author(s):  
Inka Busack ◽  
Florian Jordan ◽  
Peleg Sapir ◽  
Henrik Bringmann
Keyword(s):  
Time Scales ◽  
Biological Processes ◽  
C Elegans ◽  
Physiological Processes ◽  
Aging And Health ◽  
Mechanistic Basis ◽  
Living Organisms ◽  
And Behavior ◽  
Short Time

Optogenetics controls neural activity and behavior in living organisms through genetically targetable actuators and light. This method has revolutionized biology and medicine as it allows controlling cells with high temporal and spatial precision. Optogenetics is typically applied only at short time scales, for instance to study specific behaviors. Optogenetically manipulating behavior also gives insights into physiology, as behavior controls systemic physiological processes. For example, arousal and sleep affect aging and health span. To study how behavior controls key physiological processes, behavioral manipulations need to occur at extended time scales. However, methods for long-term optogenetics are scarce and typically require expensive compound microscope setups. Optogenetic experiments can be conducted in many species. Small model animals such as the nematode C. elegans, have been instrumental in solving the mechanistic basis of medically important biological processes. We developed OptoGenBox, an affordable stand-alone and simple-to-use device for long-term optogenetic manipulation of C. elegans. OptoGenBox provides a controlled environment and is programmable to allow the execution of complex optogenetic manipulations over long experimental times of many days to weeks. To test our device, we investigated how optogenetically increased arousal and optogenetic sleep deprivation affect survival of arrested first larval stage C. elegans. We optogenetically activated the nociceptive ASH sensory neurons using ReaChR, thus triggering an escape response and increase in arousal. In addition, we optogenetically inhibited the sleep neuron RIS using ArchT, a condition known to impair sleep. Both optogenetic manipulations reduced survival. Thus, OptoGenBox presents an affordable system to study the long-term consequences of optogenetic manipulations of key biological processes in C. elegans and perhaps other small animals.

scholarly journals Oropharyngeal Dysphagia: A Proposal for an Ecological Theoretical Model

2022 ◽  
pp. 7-16
Author(s):  
Rafaela Soares Rech ◽  
Bárbara Niegia Garcia de Goulart
Keyword(s):  
Data Analysis ◽  
Theoretical Model ◽  
Oropharyngeal Dysphagia ◽  
Theoretical Models ◽  
Epidemiological Studies ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs ◽  
Confounding Variables ◽  
Direct Causation ◽  
Definition Of

Background: The exponential growth in epidemiological studies has been reflected in an increase in analytical studies. Thus, theoretical models are required to guide the definition of data analysis, although so far, they are seldom used in Speech, Language, and Hearing Sciences. Objective: To propose a multicausal model for oropharyngeal dysphagia using directed acyclic graphs showing mediating variables, confounding variables, and variables connected by direct causation. Design: This integrative literature review. Setting: This was carried out until January 4, 2021, and searches were performed with the MEDLINE, EMBASE,and other bases.

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.

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.

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