Statistical matching for conservation science |
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Authors: | Judith Schleicher Johanna Eklund Megan D Barnes Jonas Geldmann Johan A Oldekop Julia P G Jones |
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Institution: | 1. Department of Geography, University of Cambridge, Cambridge, CB2 1QB U.K.;2. Department of Geosciences and Geography, Helsinki Institute of Sustainability Science, Faculty of Science, University of Helsinki, P.O. Box 64 (Gustaf Hällströmin katu 2A), FI-00014, Finland;3. School of Biology, The University of Queensland, St Lucia, QLD, 4067 Australia;4. Conservation Science Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ U.K.;5. Global Development Institute, University of Manchester, Oxford Road, Manchester, M13 9PL U.K.;6. College of Engineering and Environmental Sciences, Bangor University, Thoday Road, Deniol Road, LL57 2UW, U.K. |
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Abstract: | The awareness of the need for robust impact evaluations in conservation is growing and statistical matching techniques are increasingly being used to assess the impacts of conservation interventions. Used appropriately matching approaches are powerful tools, but they also pose potential pitfalls. We outlined important considerations and best practice when using matching in conservation science. We identified 3 steps in a matching analysis. First, develop a clear theory of change to inform selection of treatment and controls and that accounts for real-world complexities and potential spillover effects. Second, select the appropriate covariates and matching approach. Third, assess the quality of the matching by carrying out a series of checks. The second and third steps can be repeated and should be finalized before outcomes are explored. Future conservation impact evaluations could be improved by increased planning of evaluations alongside the intervention, better integration of qualitative methods, considering spillover effects at larger spatial scales, and more publication of preanalysis plans. Implementing these improvements will require more serious engagement of conservation scientists, practitioners, and funders to mainstream robust impact evaluations into conservation. We hope this article will improve the quality of evaluations and help direct future research to continue to improve the approaches on offer. |
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Keywords: | causal inference conservation effectiveness counterfactual impact evaluation spillover spatial autocorrelation autocorrelación espacial consecuencias indirectas efectividad de la conservación evaluación de impacto hipótesis de contraste inferencia causal 因果推论 保护有效性 溢出效应 空间自相关 反事实 效果评估 |
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