Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments
This work addresses causal inference problems for researchers and practitioners dealing with spatial data like satellite imagery, though it appears incremental as it combines existing techniques.
The paper tackles the challenge of causal inference with spatial data and continuous treatments by proposing a neural network framework integrated with Gaussian processes and propensity scores. Results show NN-based models significantly outperform linear spatial regression models in estimating causal effects across synthetic and real-world data.
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.