ArticleProceedings. Biological sciences2021
A biologist's guide to model selection and causal inference.
Article in Proceedings. Biological sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
54 citing papers in PubMed.
- Repeated Measures of Modified Rankin Scale Scores to Assess Functional Recovery From Stroke: AFFINITY Study Findings.Journal of the American Heart Association · 2022Trial
- Maternal Diet Quality Is Associated with Placental Proteins in the Placental Insulin/Growth Factor, Environmental Stress, Inflammation, and mTOR Signaling Pathways: The Healthy Start ECHO Cohort.The Journal of nutrition · 2022Trial
- Interpretable and Predictive Models Based on High-Dimensional Data in Ecology and Evolution.Molecular ecology resources · 2026Article
- Applications and reporting of causal inference modelling in infectious disease studies: A systematic review.Infectious Disease Modelling · 2026Article
- Adapting flow cytometry for studying immune cell seasonality in wild migratory bats.bioRxiv : the preprint server for biology · 2026Article
- Quantifying and comparing causal patterns in stochastic chemical reaction networks.NPJ systems biology and applications · 2026Article
- The effects of temperature on nestling growth in a songbird depend on developmental constraints.PloS one · 2026Article
- Article
- Automatic uncertainty evaluation for determining the number of components in nested models and the shrinkage regularization parameters.PloS one · 2026Article
- Viral epidemic potential is not uniformly distributed across the bat phylogeny.Communications biology · 2025Article
- Machine learning in biological research: key algorithms, applications, and future directions.BMC biology · 2025Review
- Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data.American journal of human biology : the official journal of the Human Biology Council · 2025Article
- A machine learning approach using semen parameters and sperm mitochondrial DNA copy number to predict couples' fecundity.F&S reports · 2025Article
- Increasing certainty in systems biology models using Bayesian multimodel inference.Nature communications · 2025Article
- Diverse hosts, diverse immune systems: Evolutionary variation in bat immunology.Annals of the New York Academy of Sciences · 2025Review
- Let's DAG in: how directed acyclic graphs can help behavioural ecology be more transparent.Proceedings. Biological sciences · 2025Article
- Maternal allostatic load in pregnancy is prospectively associated with child adiposity and metabolic function across infancy and early childhood.Psychoneuroendocrinology · 2025Article
- Causal drivers of mosquito abundance in urban informal settlements.Environmental research letters : ERL [Web site] · 2025Article
- Causal Effects Versus Causal Mechanisms: Two Traditions With Different Requirements and Contributions Towards Causal Understanding.Ecology letters · 2025Review
- Experience sampling method studies in physical activity research: the relevance of causal reasoning.The international journal of behavioral nutrition and physical activity · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.