# Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference

arXiv: https://arxiv.org/abs/2405.08174 · HTML: https://mlsanity.com/paper/2405.08174/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations](https://mlsanity.com/paper/2002.04083/) (2020)
- [Learn To Pay Attention](https://mlsanity.com/paper/1804.02391/) (2018)
- [Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders](https://mlsanity.com/paper/1902.00450/) (2019)
- [Identification and estimation of treatment and interference effects in observational studies on networks](https://mlsanity.com/paper/1609.06245/) (2016)
- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
- [Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey](https://mlsanity.com/paper/2101.00734/) (2021)
- [Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq](https://mlsanity.com/paper/2003.13555/) (2020)
- [A review of spatial causal inference methods for environmental and epidemiological applications](https://mlsanity.com/paper/2007.02714/) (2020)
- [Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past](https://mlsanity.com/paper/2203.06056/) (2022)
- [Spatial causal inference in the presence of unmeasured confounding and interference](https://mlsanity.com/paper/2303.08218/) (2023)
- [Generalized propensity score approach to causal inference with spatial interference](https://mlsanity.com/paper/2007.00106/) (2020)
- [Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies](https://mlsanity.com/paper/2209.12316/) (2022)
- [Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference](https://mlsanity.com/paper/2303.07122/) (2023)
- [Defining and estimating causal direct and indirect effects when setting the mediator to specific values is not feasible](https://mlsanity.com/paper/1509.00351/) (2015)

## Built on by (most cited first, 4 of 4)

- [GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding](https://mlsanity.com/paper/2502.05295/) (2025)
- [Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference](https://mlsanity.com/paper/2510.08762/) (2025)
- [Spatio-Temporal Hierarchical Causal Models](https://mlsanity.com/paper/2511.20558/) (2025)
- [Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework](https://mlsanity.com/paper/2602.03459/) (2026)

## Abstract (arXiv)

Spatial interference (SI) occurs when the treatment at one location affects the outcomes at other locations. Accounting for spatial interference in spatiotemporal settings poses further challenges as interference violates the stable unit treatment value assumption, making it infeasible for standard causal inference methods to quantify the effects of time-varying treatment at spatially varying outcomes. In this paper, we first formalize the concept of spatial interference in case of time-varying treatment assignments by extending the potential outcome framework under the assumption of no unmeasured confounding. We then propose our deep learning based potential outcome model for spatiotemporal causal inference. We utilize latent factor modeling to reduce the bias due to time-varying confounding while leveraging the power of U-Net architecture to capture global and local spatial interference in data over time. Our causal estimators are an extension of average treatment effect (ATE) for estimating direct (DATE) and indirect effects (IATE) of spatial interference on treated and untreated data. Being the first of its kind deep learning based spatiotemporal causal inference technique, our approach shows advantages over several baseline methods based on the experiment results on two synthetic datasets, with and without spatial interference. Our results on real-world climate dataset also align with domain knowledge, further demonstrating the effectiveness of our proposed method.