ReviewEcology letters2025
Causal Inference With Observational Data and Unobserved Confounding Variables.
Review in Ecology letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Unifying bright spots research for people and nature.Nature ecology & evolution · 2026Review
- From environmental heterogeneity to stability: Beta-diversity stabilizes biomass across space.Ecology · 2026Article
- From Prediction to Creation: Generative Plant Design.Plants (Basel, Switzerland) · 2026Review
- Unobserved confounders cannot explain over-crediting in avoided deforestation carbon projects.Nature ecology & evolution · 2026Article
- Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.Briefings in bioinformatics · 2026Review
- From kelp forests to turf reefs: Patterns, drivers, and impacts to functional diversity.Ecology · 2026Article
- Biology Needs Philosophy, But What Philosophy?Bioscience · 2026Review
- Marginal causal effect of fungicide seed treatments on soybean yield and uncertain profitability in the US Midwest.Scientific reports · 2026Article
- Clinical impact and cost-effectiveness of rapid versus non-rapid antiretroviral therapy initiation in HIV-positive men who have sex with men in China using modelling and 96-week multicenter cohort data.Scientific reports · 2026Article
- The relationship between low birth weight and neurological disorders: a prospective cohort study in the UK Biobank.BMC neurology · 2026Article
- Identifying the causal effects of photovoltaic installations on grassland productivity using double machine learning: a case study in inner Mongolia.Scientific reports · 2026Article
- Investigating the Yanomami malaria outbreak: gold mining and malaria.Biology letters · 2026Article
- Effects of multimodal analgesia with combined quadratus lumborum block on enhanced recovery after Kasai portoenterostomy in infants with biliary atresia: a retrospective cohort study.Frontiers in pediatrics · 2026Article
- Persistent loss of animal diversity on a rocky shore over nine decades and across multiple investigators.PeerJ · 2026Article
- Assessing direct effects of insect change on insectivore populations in the United Kingdom.Biodiversity and conservation · 2026Article
- Riparian vegetation reduces coastal turbidity.Communications sustainability · 2026Article
- Towards a systematic framework to assess restoration success of interventions in coral reef ecosystems.PloS one · 2026Article
- Polycystic ovary syndrome and miscarriage risk: dual evidence from cohort study and Mendelian randomization.Frontiers in endocrinology · 2026Article
- Causal inference unveils how forest coverage mitigates excess snakebite cases during rainfall seasons in Colombia.Scientific reports · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Experiments have long been the gold standard for causal inference in Ecology. As Ecology tackles progressively larger problems, however, we are moving beyond the scales at which randomised controlled experiments are feasible. To answer causal questions at scale, we need to also use observational data -something Ecologists tend to view with great scepticism. The major challenge using observational data for causal inference is confounding variables: variables affecting both a causal variable and response of interest. Unmeasured confounders-known or unknown-lead to statistical bias, creating spurious correlations and masking true causal relationships. To combat this omitted variable bias, other disciplines have developed rigorous approaches for causal inference from observational data that flexibly control for broad suites of confounding variables. We show how ecologists can harness some of these methods-causal diagrams to identify confounders coupled with nested sampling and statistical designs-to reduce risks of omitted variable bias. Using an example of estimating warming effects on snails, we show how current methods in Ecology (e.g., mixed models) produce incorrect inferences due to omitted variable bias and how alternative methods can eliminate it, improving causal inferences with weaker assumptions. Our goal is to expand tools for causal inference using observational and imperfect experimental data in Ecology.
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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.