Abstract
In ecology, causal questions are ubiquitous, yet the literature describing systematic approaches to answering these questions is vast and fragmented across different traditions (e.g., randomization, structural equation modeling, convergent cross mapping). In our Perspective, we connect the causal assumptions, tasks, frameworks, and methods across these traditions, thereby providing a synthesis of the concepts and methodological advances for detecting and quantifying causal relationships in ecological systems. Through a newly developed workflow, we emphasize how ecologists’ choices among empirical approaches are guided by the pre-existing knowledge that ecologists have and the causal assumptions that ecologists are willing to make.
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References
Laland, K. N., Sterelny, K., Odling-Smee, J., Hoppitt, W. & Uller, T. Cause and effect in biology revisited: is Mayr’s proximate-ultimate dichotomy still useful? Science 334, 1512–1516 (2011).
Mayr, E. Cause and effect in biology. Science 134, 1501–1506 (1961).
Woodward, J. Causation in biology: stability, specificity, and the choice of levels of explanation. Biol. Philos. 25, 287–318 (2010).
Ben-Menahem, Y. Causation in Science (Princeton University Press, 2018).
Wagner, A. Causality in complex systems. Biol. Philos. 14, 83–101 (1999).
Poliseli, L., Coutinho, J. G. E., Viana, B., Russo, F. & El-Hani, C. N. Philosophy of science in practice in ecological model building. Biol. Philos. 37, 21 (2022).
Raerinne, J. Causal and mechanistic explanations in ecology. Acta Biotheor. 59, 251–271 (2011).
Woodward, J. Making Things Happen: A Theory of Causal Explanation (Oxford University Press, 2004).
Shipley, B. Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations and Causal Inference with R (Cambridge University Press, Cambridge, UK, 2016).
Ross, L. N. Causes with material continuity. Biol. Philos. 36, 52 (2021).
Rosenbaum, P. R. Known effects. In Observational Studies 136–153 (Springer New York, New York, NY, 1995).
Pearl, J. Causality: Models, Reasoning, and Inference (Cambridge University Press, Cambridge, U.K., 2009).
Dominici, F., Bargagli-Stoffi, F. J. & Mealli, F. From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework. Harv. Data Sci. Rev. https://doi.org/10.1162/99608f92.8102afed (2021).
Estes, J. A. & Palmisano, J. F. Sea otters: their role in structuring nearshore communities. Science 185, 1058–1060 (1974).
Estes, J. E., Smith, N. S. & Palmisano, J. F. Sea otter predation and community organization in the Western Aleutian Islands, Alaska. Ecology 59, 822–833 (1978).
Sagarin, R. & Pauchard, A. Observational approaches in ecology open new ground in a changing world. Front. Ecol. Environ. 8, 379–386 (2010).
Sagarin, R. & Pauchard, A. Observation and Ecology: Broadening the Scope of Science to Understand a Complex World (Island Press/Center for Resource Economics, Washington, DC, 2012).
Benedetti-Cecchi, L. et al. Hybrid datasets: integrating observations with experiments in the era of macroecology and big data. Ecology 99, 2654–2666 (2018).
De Boeck, H. J. et al. Global change experiments: challenges and opportunities. BioScience 65, 922–931 (2015).
McCleery, R. et al. Uniting experiments and big data to advance ecology and conservation. Trends Ecol. Evol. 38, 970–979 (2023).
Wootten, T. & Pfister, C. The motivation for and context of experiments in ecology. in Experimental Ecology: Issues and Perspectives (Oxford University Press, 1998).
Dawid, P. Causal inference without counterfactuals. J. Am. Stat. Assoc. 95, 407–424 (2000).
Holland, P. W. Statistics and causal inference. J. Am. Stat. Assoc. 81, 945–960 (1986).
Imbens, G. W. & Rubin, D. B. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (Cambridge University Press, 2015).
Rubin, D. B. Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66, 688–701 (1974).
Rubin, D. B. Causal inference using potential outcomes: design, modeling, decisions. J. Am. Stat. Assoc. 100, 322–331 (2005).
Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496–500 (2012).
Spirtes, P., Glymour, C. N. & Scheines, R. Causation, Prediction, and Search (MIT Press, Cambridge, MA, 2000).
D’Onofrio, B. M. et al. Causal inferences regarding prenatal alcohol exposure and childhood externalizing problems. Arch. Gen. Psychiatry 64, 1296–1304 (2007).
Pearce, N., Vandenbroucke, J. P. & Lawlor, D. A. Causal inference in environmental. Epidemiology 30, 311–316 (2019).
Pingault, J.-B. et al. Using genetic data to strengthen causal inference in observational research. Nat. Rev. Genet. 19, 566–580 (2018).
White, R. F. et al. Recent research on Gulf War illness and other health problems in veterans of the 1991 Gulf War: effects of toxicant exposures during deployment. Cortex 74, 449–475 (2016).
Beck, B., Antonelli, J. & Piñeros, G. Effects of New York City’s neighborhood policing policy. Police Q. 25, 470–496 (2022).
Wikström, P.-O. H. & Kroneberg, C. Analytic criminology: mechanisms and methods in the explanation of crime and its causes. Annu. Rev. Criminol. 5, 179–203 (2022).
Jacob, B. A. & Lefgren, L. Remedial education and student achievement: a regression-discontinuity analysis. Rev. Econ. Stat. 86, 226–244 (2004).
Long, B. T. & Kurlaender, M. Do community colleges provide a viable pathway to a baccalaureate degree? Educ. Eval. Policy Anal. 31, 30–53 (2009).
Brewer, D., Dench, D. & Taylor, L. O. Advances in causal inference at the intersection of air pollution and health outcomes. Annu. Rev. Resour. Econ. 15, 455–469 (2023).
National Academies of Sciences, Engineering, and Medicine et al. Definition of causality. In Advancing the Framework for Assessing Causality of Health and Welfare Effects to Inform National Ambient Air Quality Standard Reviews (National Academies Press, Washington, DC, 2022).
International Agency for Research on Cancer. Non-ionizing radiation, Part 2: radiofrequency electromagnetic fields. In IARC Monographs on the Evaluation of Carcinogenic Risks to Humans vol. 102 (IARC, Lyon, France, 2013).
Yuan, A. E. & Shou, W. Data-driven causal analysis of observational biological time series. eLife 11, e72518 (2022).
Arif, S. & MacNeil, M. A. Applying the structural causal model framework for observational causal inference in ecology. Ecol. Monogr. 93, e1554 (2022).
Butsic, V., Lewis, D. J., Radeloff, V. C., Baumann, M. & Kuemmerle, T. Quasi-experimental methods enable stronger inferences from observational data in ecology. Basic Appl. Ecol. 19, 1–10 (2017).
Grace, J. B. & Irvine, K. M. Scientist’s guide to developing explanatory statistical models using causal analysis principles. Ecology 101, e02962 (2020).
Larsen, A. E., Meng, K. & Kendall, B. E. Causal analysis in control–impact ecological studies with observational data. Methods Ecol. Evol. 10, 924–934 (2019).
Ramsey, D. S. L. et al. Using propensity scores for causal inference in ecology: Options, considerations, and a case study. Methods Ecol. Evol. 10, 320–331 (2019).
Grace, J. B., Scheiner, S. M. & Schoolmaster, Jr., D. R. Structural equation modeling: building and evaluating causal models. In Ecological Statistics (eds Fox, G. A. et al.) 168–199 (Oxford University Press, Oxford, 2015).
Paul, W. L. A causal modelling approach to spatial and temporal confounding in environmental impact studies. Environmetrics 22, 626–638 (2011).
Dee, L. E. et al. Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference. Nat. Commun. 14, 2607 (2023).
Siegel, K. J., Larsen, L., Stephens, C., Stewart, W. & Butsic, V. Quantifying drivers of change in social-ecological systems: land management impacts wildfire probability in forests of the western US. Reg. Environ. Chang. 22, 98 (2022).
Kimmel, K., Dee, L. E., Avolio, M. L. & Ferraro, P. J. Causal assumptions and causal inference in ecological experiments. Trends Ecol. Evol. 36, 1141–1152 (2021).
Rubin, D. B. For objective causal inference, design trumps analysis. Ann. Appl. Stat. 2, 808–840 (2008).
Shadish, W. R., Cook, T. D. & Campbell, D. T. Experimental and Quasi-Experimental Designs for Generalized Causal Inference (Houghton Mifflin, Boston, 2001).
Glymour, C., Zhang, K. & Spirtes, P. Review of causal discovery methods based on graphical models. Front. Genet. 10, 524 (2019).
Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019).
Harnack, D., Laminski, E., Schünemann, M. & Pawelzik, K. R. Topological causality in dynamical systems. Phys. Rev. Lett. 119, 098301 (2017).
Shi, J., Chen, L. & Aihara, K. Embedding entropy: a nonlinear measure of dynamical causality. J. R. Soc. Interface 19, 20210766 (2022).
Hernán, M. A. & Robins, J. M. Causal Inference: What If (CRC Press, Boca Raton, 2025).
Pearl, J. Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009).
Robins, J. M. & Wasserman, L. On the impossibility of inferring causation from association without background knowledge. In Computation, Causation, and Discovery (eds Cooper, G. F. & Glymour, C.) (MIT Press, 1999).
Pearl, J. The foundations of causal inference. Soc. Methodol. 40, 75–149 (2010).
Imbens, G. W. & Angrist, J. D. Identification and estimation of local average treatment effects. Econometrica 62, 467 (1994).
Imai, K., Keele, L., Tingley, D. & Yamamoto, T. Unpacking the black box of causality: learning about causal mechanisms from experimental and observational studies. Am. Polit. Sci. Rev. 105, 765–789 (2011).
Granger, C. W. J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424 (1969).
Reichenbach, H. The Direction of Time (University of California Press, Berkeley, 1956).
Kiiveri, H. T., Speed, T. P. & Carlin, J. B. Recursive causal models. J. Aust. Math. Soc. Ser. Pure Math. Stat. 36, 30–52 (1984).
Scheines, R. An introduction to causal inference. In Causality In Crisis? Statistical Methods & Search for Causal Knowledge in Social Sciences. (eds McKim, V. R. & Turner, S. P.) 185–200 (University of Notre Dame Press, Notre Dame, IN, USA, 1997).
Addicott, E. T., Fenichel, E. P., Bradford, M. A., Pinsky, M. L. & Wood, S. A. Toward an improved understanding of causation in the ecological sciences. Front. Ecol. Environ. 20, 474–480 (2022).
Arif, S. & MacNeil, M. A. Predictive models aren’t for causal inference. Ecol. Lett. 25, 1741–1745 (2022).
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, New York, NY, 2010).
Grace, J. B. An integrative paradigm for building causal knowledge. Ecol. Monogr. 94, e1628 (2024).
Greenland, S., Pearl, J. & Robins, J. M. Confounding and collapsibility in causal inference. Stat. Sci. 14, 29–46 (1999).
Shpitser, I., VanderWeele, T. & Robins, J. M. On the validity of covariate adjustment for estimating causal effects. In Proc. Twenty-Sixth Conference on Uncertainty in Artificial Intelligence 527–536 (AUAI Press, Arlington, Virginia, USA, 2010).
Rosenbaum, P. R. Choice as an alternative to control in observational studies. Stat. Sci. 14, 259–304 (1999).
Gelman, A., Hill, J. & Vehtari, A. Causal inference using regression on the treatment variable. In Regression and Other Stories (eds Gelman, A., Hill, J. & Vehtari, A.) 363–382 (Cambridge University Press, Cambridge, 2020).
Wiik, E. et al. Mechanisms and impacts of an incentive-based conservation program with evidence from a randomized control trial. Conserv. Biol. 34, 1076–1088 (2020).
Chickering, D. M. Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, 507–554 (2003).
Pearl, J. Causal diagrams for empirical research. Biometrika 82, 669–688 (1995).
Kunicki, Z. J., Smith, M. L. & Murray, E. J. A primer on structural equation model diagrams and directed acyclic graphs: when and how to use each in psychological and epidemiological research. Adv. Methods Pract. Psychol. Sci. 6, 251524592311560 (2023).
Pearl, J. The causal foundations of structural equation modeling. In Handbook of Structural Equation Modeling 68–91 (Guilford Press, New York, NY, US, 2012).
Hernán, M. A., Wang, W. & Leaf, D. E. Target trial emulation: a framework for causal inference from observational data. JAMA 328, 2446 (2022).
Hernán, M. A., Dahabreh, I. J., Dickerman, B. A. & Swanson, S. A. The target trial framework for causal inference from observational data: why and when is it helpful? Ann. Intern. Med. 178, 402–407 (2025).
Lundberg, I., Johnson, R. & Stewart, B. M. What is your estimand? Defining the target quantity connects statistical evidence to theory. Am. Sociol. Rev. 86, 532–565 (2021).
Spake, R. et al. Understanding ‘it depends’ in ecology: a guide to hypothesising, visualising and interpreting statistical interactions. Biol. Rev. 98, 983–1002 (2023).
Correia, H. E., Dee, L. E. & Ferraro, P. J. Designing causal mediation analyses to quantify intermediary processes in ecology. Biol. Rev. Camb. Philos. Soc. 100, 1512–1533 (2025).
Paluš, M. From nonlinearity to causality: statistical testing and inference of physical mechanisms underlying complex dynamics. Contemp. Phys. 48, 307–348 (2007).
Dawid, P. The decision-theoretic approach to causal inference. In Wiley Series in Probability and Statistics (eds Berzuini, C., Dawid, P. & Bernardinelli, L.) 25–42 (Wiley, 2012).
Ibeling, D. & Icard, T. Comparing causal frameworks: potential outcomes, structural models, graphs, and abstractions. In Advances in Neural Information Processing Systems (eds Oh, A. et al.) vol. 36 80130–80141 (Curran Associates, Inc., 2023).
Pearl, J. Graphical models, potential outcomes and causal inference: Comment on Linquist and Sobel. NeuroImage 58, 770–771 (2011).
Weinberger, N. Comparing Rubin and Pearl’s causal modelling frameworks: a commentary on Markus (2021). Econ. Philos. 39, 485–493 (2023).
Bollen, K. A. & Pearl, J. Eight myths about causality and structural equation models. In Handbook of Causal Analysis for Social Research (ed. Morgan, S. L.) 301–328 (Springer Netherlands, Dordrecht, 2013).
Wiener, N. Modern Mathematics for Engineers (McGraw-Hill, New York, 1956).
Zhao, A. & Ding, P. Regression-based causal inference with factorial experiments: estimands, model specifications and design-based properties. Biometrika 109, 799–815 (2022).
Imai, K., King, G. & Stuart, E. A. Misunderstandings between experimentalists and observationalists about causal inference. J. R. Stat. Soc. Ser. A Stat. Soc. 171, 481–502 (2008).
Bulbulia, J. A. Methods in causal inference. Part 4: confounding in experiments. Evol. Hum. Sci. 6, e43 (2024).
Rubin, D. B. Bayesian inference for causal effects: the role of randomization. Ann. Stat. 6, 34–58 (1978).
Smokorowski, K. E. & Randall, R. G. Cautions on using the before-after-control-impact design in environmental effects monitoring programs. FACETS 2, 212–232 (2017).
Gelman, A. & Hill, J. Causal inference using multilevel models. In Data Analysis Using Regression and Multilevel/Hierarchical Models 503–512 (Cambridge University Press, 2006).
Cousineau, M., Verter, V., Murphy, S. A. & Pineau, J. Estimating causal effects with optimization-based methods: a review and empirical comparison. Eur. J. Oper. Res. 304, 367–380 (2023).
Igelström, E. et al. Causal inference and effect estimation using observational data. J. Epidemiol. Community Health 76, 960 (2022).
Huang, M. Y. Sensitivity analysis for the generalization of experimental results. J. R. Stat. Soc. Ser. A Stat. Soc. 187, 900–918 (2024).
Rosenbaum, P. R. Sensitivity to hidden bias. In Observational Studies 105–170 (Springer New York, New York, NY, 1995).
Shen, C., Li, X., Li, L. & Were, M. C. Sensitivity analysis for causal inference using inverse probability weighting. Biom. J. 53, 822–837 (2011).
VanderWeele, T. J. & Arah, O. A. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology 22, 42–52 (2011).
Yadlowsky, S., Namkoong, H., Basu, S., Duchi, J. & Tian, L. Bounds on the conditional and average treatment effect with unobserved confounding factors. Ann. Stat. 50, 2587–2615 (2022).
Sullivan, A. J. & VanderWeele, T. J. Bias and sensitivity analysis for unmeasured confounders in linear structural equation models. Preprint at https://doi.org/10.48550/ARXIV.2103.05775 (2021).
Rosenbaum, P. R. The role of known effects in observational studies. Biometrics 45, 557 (1989).
Rosenbaum, P. R. Sensitivity analyses informed by tests for bias in observational studies. Biometrics 79, 475–487 (2023).
Rothman, K. J., Greenland, S. & Lash, T. L. Validity in epidemiologic studies. In Modern Epidemiology 128–147 (Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia, 2008).
Greenland, S. & Lash, T. L. Bias Analysis. In Modern Epidemiology 128–147 (Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia, 2008).
Bareinboim, E., Tian, J. & Pearl, J. Recovering from selection bias in causal and statistical inference. In Probabilistic and Causal Inference (eds Geffner, H., Dechter, R. & Halpern, J. Y.) 433–450 (ACM, New York, NY, USA, 2022).
Hernán, M. A., Hernández-Díaz, S. & Robins, J. M. A structural approach to selection bias. Epidemiology 15, 615–625 (2004).
Imai, K. & Yamamoto, T. Causal inference with differential measurement error: nonparametric identification and sensitivity analysis. Am. J. Polit. Sci. 54, 543–560 (2010).
Pearl, J. On measurement bias in causal inference. In Proc. Twenty-Sixth Conference on Uncertainty in Artificial Intelligence 425–432 (AUAI Press, Arlington, Virginia, USA, 2010).
Valeri, L. Measurement error in causal inference. In Handbook of Measurement Error (eds Yi, G. Y. et al.) (CRC Press, Boca Raton, 2022).
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S. & Sejdinovic, D. Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019).
Kummerfeld, E., Williams, L. & Ma, S. Power analysis for causal discovery. Int. J. Data Sci. Anal. 17, 289–304 (2024).
Li, J., Liu, L., Le, T. D. & Liu, J. Accurate data-driven prediction does not mean high reproducibility. Nat. Mach. Intell. 2, 13–15 (2020).
Tredennick, A. T., Hooker, G., Ellner, S. P. & Adler, P. B. A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology 102, e03336 (2021).
Acknowledgements
This work emerged partly from discussions at the workshop Causality in Ecology on August 21–23, 2023, in Baltimore, MD, USA. We thank Johns Hopkins University for funding and Rachel Pickett, Carter Polston, Kip Hinton, and Shang Jones for assistance in hosting the workshop. We thank Ashley E. Larsen for insightful discussions during the workshop and feedback on drafts of the paper. H.E.C and P.J.F. acknowledge funding support from USDA-NIFA award 2023-67023-39033.
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H.E.C. led the paper. H.E.C., L.E.D. and P.J.F. co-organized, and P.J.F. funded, the workshop in which J.E.K.B., H.E.C., L.E.D., J.R.F., P.J.F., M.-J.F., C.G., J.R., B.S., I.S., K.J.S., G.S., and B.vH. contributed to establishing the goals and emphases of the paper. H.E.C., L.E.D., and P.J.F. initiated the paper concept and framing. H.E.C. and P.J.F. wrote the main text. J.E.K.B., L.E.D., J.R.F., M.-J.F., J.R., B.S., I.S., K.J.S., G.S., and B.vH. suggested edits to the drafts of the paper. H.E.C. conceived and wrote the Supplementary Information.
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Correia, H.E., Dee, L.E., Byrnes, J.E.K. et al. Best practices for moving from correlation to causation in ecological research.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-69878-z
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DOI: https://doi.org/10.1038/s41467-026-69878-z
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