  Marine conservation preserves fish biodiversity, protects marine and coastal
ecosystems, and supports climate resilience and adaptation. Despite the
importance of establishing marine protected areas (MPAs), research on the
effectiveness of MPAs with different conservation policies is limited due to
the lack of quantitative MPA information. In this paper, leveraging a global
MPA database, we investigate the causal impact of MPA policies on fish
biodiversity. To address challenges posed by this clustered and confounded
observational study, we construct a matching estimator of the average treatment
effect and a cluster-weighted bootstrap method for variance estimation. We
establish the theoretical guarantees of the matching estimator and its variance
estimator. Under our proposed matching framework, we recommend matching on both
cluster-level and unit-level covariates to achieve efficiency. The simulation
results demonstrate that our matching strategy minimizes the bias and achieves
the nominal confidence interval coverage. Applying our proposed matching method
to compare different MPA policies reveals that the no-take policy is more
effective than the multi-use policy in preserving fish biodiversity.
