用神经网络和高斯过程处理空间数据的连续干预因果推断
Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments
- 结合神经网络与近似高斯过程，建模空间干扰与未观测混杂
- 在真实卫星数据中，神经网络模型显著优于线性回归模型
- 适合需精准空间因果分析的环境科学与政策决策场景
空间数据中的因果推理常因高维输入而困难。为此，我们提出一种基于神经网络（NN）的框架，融合近似高斯过程以处理空间干扰与未观测混杂。同时，采用广义倾向得分方法，解决连续处理下部分观测结果的因果效应估计问题。我们在合成数据、半合成数据及从卫星影像推断的真实世界数据上评估该框架。结果表明，基于神经网络的模型在估计因果效应方面显著优于线性空间回归模型。此外，在真实案例研究中，神经网络模型提供了更合理的因果效应预测，有助于相关应用中的决策制定。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。