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.
Highlights
There have been numerous calls for conservation science to provide a stronger evidence base for policy and practice (Pullin & Knight 2001; Sutherland et al 2004; Baylis et al 2016)
Three key steps when using matching for impact evaluations
The second step consists of selecting appropriate covariates and the specific matching approach
Summary
There have been numerous calls for conservation science to provide a stronger evidence base for policy and practice (Pullin & Knight 2001; Sutherland et al 2004; Baylis et al 2016). The few matching studies that have accounted for differences between land use restrictions have found that the degree to which conservation interventions can be considered effective is influenced by how control areas are defined and selected (Gaveau et al 2012; Schleicher et al 2017).
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