Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments

• URL: http://arxiv.org/abs/2412.04285v1
• Date: Thu, 05 Dec 2024 16:06:23 GMT
• Title: Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments
• Authors: Ziyang Jiang, Zach Calhoun, Yiling Liu, Lei Duan, David Carlson,
• Abstract summary: We propose a neural network (NN) based framework integrated with an approximate Gaussian process to manage spatial interference and unobserved confounding. We evaluate our framework using synthetic, semi-synthetic, and real-world data inferred from satellite imagery.
• Score: 2.2236425765564753
• License:
• Abstract: 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.

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