Selecting among counterfactual methods to evaluate conservation interventions

Journal article

Effective conservation that benefits biodiversity and human well‐being is imperative for global sustainability. Achieving this requires rigorous evaluation of conservation policies and programs to understand their causal effects on environmental and social outcomes. Counterfactual impact evaluation methods offer a robust framework for assessing intervention impacts by comparing observed outcomes with hypothetical alternative scenarios (the ‘counterfactual’). Despite recent advances, a significant research‐implementation gap persists in applying these methods within conservation practice. This paper is intended to help conservation practitioners, scientists, and funders respond to the growing demand for causal evaluations by providing an introductory overview of key counterfactual evaluation methods. It introduces a decision framework to guide the selection of appropriate evaluation methods according to project‐specific parameters, such as project goals and timing. Application of the framework is illustrated through examples including community‐managed fisheries, camera traps, and payments for ecosystem services. These examples highlight that the most appropriate evaluation method depends on various factors, such as whether the intervention can be randomized, available sample size, and data availability from both intervention and non‐intervention sites. By providing a structured approach to selecting counterfactual methods for specific conservation projects, this paper aims to stimulate broader adoption of evidence‐based practices in conservation.

Authors: O'Garra, T; Martin, R; Pynegar, E; Polo‐Urrea, C; Eklund, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Conservation Science and Practice
• Article number: e70066
• Publication date: 2025-06-17
• Acceptance date: 2025-05-06
• DOI: 10.1111/csp2.70066
• EISSN: 2578-4854
• ISSN: 2578-4854
• Language: English
• Keywords: impact evaluation; conservation interventions; causal inference; counterfactual methods