Causal reasoning is often challenging with spatial data, particularly when handling high-dimensional inputs. To address this, we propose a neural network (NN) based framework integrated with an approximate Gaussian process to manage spatial interference and unobserved confounding. Additionally, we adopt a generalized propensity-score-based approach to address partially observed outcomes when estimating causal effects with continuous treatments. We evaluate our framework using synthetic, semi-synthetic, and real-world data inferred from satellite imagery. Our results demonstrate that NN-based models significantly outperform linear spatial regression models in estimating causal effects. Furthermore, in real-world case studies, NN-based models offer more reasonable predictions of causal effects, facilitating decision-making in relevant applications.

翻译：在空间数据中进行因果推理通常具有挑战性，尤其是在处理高维输入时。为此，我们提出了一个基于神经网络（NN）的框架，该框架与近似高斯过程相结合，以管理空间干扰和未观测到的混杂因素。此外，在估计连续处理下的因果效应时，我们采用了一种基于广义倾向得分的方法来处理部分观测到的结果。我们使用合成的、半合成的以及从卫星影像推断出的真实世界数据来评估我们的框架。我们的结果表明，在估计因果效应方面，基于神经网络的模型显著优于线性空间回归模型。此外，在真实世界的案例研究中，基于神经网络的模型能够提供更合理的因果效应预测，从而促进了相关应用中的决策制定。