Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916509258874880 |
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| author | Jiang, Ziyang Calhoun, Zach Liu, Yiling Duan, Lei Carlson, David |
| author_facet | Jiang, Ziyang Calhoun, Zach Liu, Yiling Duan, Lei Carlson, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04285 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments Jiang, Ziyang Calhoun, Zach Liu, Yiling Duan, Lei Carlson, David Machine Learning J.2 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. |
| title | Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments |
| topic | Machine Learning J.2 |
| url | https://arxiv.org/abs/2412.04285 |