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.