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

arXiv: https://arxiv.org/abs/2412.04285 · HTML: https://mlsanity.com/paper/2412.04285/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 71 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Spatial causal inference in the presence of unmeasured confounding and interference](https://mlsanity.com/paper/2303.08218/) (2023)
- [Causal Inference for Spatial Treatments](https://mlsanity.com/paper/2011.00373/) (2020)
- [Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators](https://mlsanity.com/paper/2306.07918/) (2023)
- [Instrumental variables, spatial confounding and interference](https://mlsanity.com/paper/2103.00304/) (2021)

## Abstract (arXiv)

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.