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Marine conservation preserves fish biodiversity, protects marine and coastalecosystems, and supports climate resilience and adaptation. Despite theimportance of establishing marine protected areas (MPAs), research on theeffectiveness of MPAs with different conservation policies is limited due tothe lack of quantitative MPA information. In this paper, leveraging a globalMPA database, we investigate the causal impact of MPA policies on fishbiodiversity. To address challenges posed by this clustered and confoundedobservational study, we construct a matching estimator of the average treatmenteffect and a cluster-weighted bootstrap method for variance estimation. Weestablish the theoretical guarantees of the matching estimator and its varianceestimator. Under our proposed matching framework, we recommend matching on bothcluster-level and unit-level covariates to achieve efficiency. The simulationresults demonstrate that our matching strategy minimizes the bias and achievesthe nominal confidence interval coverage. Applying our proposed matching methodto compare different MPA policies reveals that the no-take policy is moreeffective than the multi-use policy in preserving fish biodiversity.

Matching Estimators of Causal Effects in Clustered Observational Studies  with Application to Quantifying the Impact of Marine Protected Areas on  Biodiversity