Marine Protected Areas (MPAs) have been established globally to conserve marine resources. Given their maintenance costs and impact on commercial fishing, it is critical to evaluate their effectiveness to support future conservation. In this paper, we use data collected from the Australian coast to estimate the effect of MPAs on biodiversity. Environmental studies such as these are often observational, and processes of interest exhibit spatial dependence, which presents challenges in estimating the causal effects. Spatial data can also be subject to preferential sampling, where the sampling locations are related to the response variable, further complicating inference and prediction. To address these challenges, we propose a spatial causal inference method that simultaneously accounts for unmeasured spatial confounders in both the sampling process and the treatment allocation. We prove the identifiability of key parameters in the model and the consistency of the posterior distributions of those parameters. We show via simulation studies that the causal effect of interest can be reliably estimated under the proposed model. The proposed method is applied to assess the effect of MPAs on fish biomass. We find evidence of preferential sampling and that properly accounting for this source of bias impacts the estimate of the causal effect.

翻译：海洋保护区（MPAs）已在全球范围内建立以保护海洋资源。考虑到其维护成本及对商业捕捞的影响，评估其保护成效对支持未来保育工作至关重要。本文利用澳大利亚海岸收集的数据，评估海洋保护区对生物多样性的影响。此类环境研究通常为观测性研究，且所关注的过程呈现空间依赖性，这为估计因果效应带来了挑战。空间数据还可能受到偏好性采样的影响，即采样点位置与响应变量相关，这进一步增加了推断与预测的复杂性。为应对这些挑战，我们提出一种空间因果推断方法，该方法同时考虑了采样过程与处理分配中未测量的空间混杂因素。我们证明了模型中关键参数的可识别性以及这些参数后验分布的一致性。通过模拟研究，我们表明在提出的模型下可以可靠地估计目标因果效应。所提方法被应用于评估海洋保护区对鱼类生物量的影响。我们发现了偏好性采样的证据，并证明恰当考虑这一偏差来源会影响因果效应的估计结果。