RevieweLife2022
Data-driven causal analysis of observational biological time series.
Review in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Best practices for moving from correlation to causation in ecological research.Nature communications · 2026Review
- Reconstructing Actin Dynamics of the Leading Edge from Observational Data.bioRxiv : the preprint server for biology · 2026Article
- Article
- Assessing the impact of interregional mobility on COVID19 spread in Spain using transfer entropy.Scientific reports · 2025Article
- Medical laboratory data-based models: opportunities, obstacles, and solutions.Journal of translational medicine · 2025Review
- MIMIC: a Python package for simulating, inferring, and predicting microbial community interactions and dynamics.Bioinformatics (Oxford, England) · 2025Article
- Empirical methods that provide physical descriptions of dynamic cellular processes.Biophysical journal · 2025Review
- Inferring directed spectral information flow between mixed-frequency time series.Research square · 2025Article
- Electrophysiological Alterations in the Progression of Parkinson's Disease and the Therapeutic Effect of Tetrabenazine on Rats With Levodopa-Induced Dyskinesia.CNS neuroscience & therapeutics · 2025Article
- Inferring causal interplay between air pollution and meteorology.Frontiers in big data · 2025Article
- Causal effect of PMFrontiers in big data · 2025Article
- A rigorous and versatile statistical test for correlations between stationary time series.PLoS biology · 2024Article
- Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach.Heliyon · 2024Article
- CAT Bridge: an efficient toolkit for gene-metabolite association mining from multiomics data.GigaScience · 2024Article
- Article
- Population impact of fine particulate matter on tuberculosis risk in China: a causal inference.BMC public health · 2023Article
- A framework for the detection and attribution of biodiversity change.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called 'model-free' causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AIV0ttQrjK8).
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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.