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ABSTRACT Ecology often seeks to answer causal questions, and while ecologists have a rich history of experimental approaches, novel observational data streams and the need to apply insights across naturally occurring conditions pose opportunities and challenges. Other fields have developed causal inference approaches that can enhance and expand our ability to answer ecological causal questions using observational or experimental data. However, the lack of comprehensive resources applying causal inference to ecological settings and jargon from multiple disciplines creates barriers. We introduce approaches for causal inference, discussing the main frameworks for counterfactual causal inference, how causal inference differs from other research aims and key challenges; the application of causal inference in experimental and quasi‐experimental study designs; appropriate interpretation of the results of causal inference approaches given their assumptions and biases; foundational papers; and the data requirements and trade‐offs between internal and external validity posed by different designs. We highlight that these designs generally prioritise internal validity over generalisability. Finally, we identify opportunities and considerations for ecologists to further integrate causal inference with synthesis science and meta‐analysis and expand the spatiotemporal scales at which causal inference is possible. We advocate for ecology as a field to collectively define best practices for causal inference. 

As climate change increasingly affects biodiversity and ecosystem services, a key challenge in ecology is accurate attribution of these impacts. Though experimental studies have greatly advanced our understanding of climate change impacts on ecological systems, experimental results are difficult to generalize to real-world scenarios. To better capture realized impacts, ecologists can use observational data. Disentangling cause and effect using observational data, however, requires careful research design. Here we describe advances in causal inference that can improve climate change attribution in observational settings. Our framework includes five steps: 1) describe the theoretical foundation, 2) choose appropriate observational data sets, 3) design a causal inference analysis, 4) estimate a counterfactual scenario, and 5) evaluate assumptions and results using robustness checks. We then demonstrate this framework using a case study focused on detecting climate change impacts on whitebark pine growth in California’s Sierra Nevada. We conclude with a discussion of challenges and frontiers in ecological climate change attribution. Our aim is to provide an accessible foundation for applying observational causal inference to climate change attribution in ecology.