# Causal Inference for Spatial Treatments

arXiv: https://arxiv.org/abs/2011.00373 · HTML: https://mlsanity.com/paper/2011.00373/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Causal Effect Inference for Structured Treatments](https://mlsanity.com/paper/2106.01939/) (2021)
- [Policy Evaluation for Temporal and/or Spatial Dependent Experiments](https://mlsanity.com/paper/2202.10887/) (2022)
- [Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments](https://mlsanity.com/paper/2403.17285/) (2024)
- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (2023)
- [Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments](https://mlsanity.com/paper/2412.04285/) (2024)

## Abstract (arXiv)

Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between units near realized treatment locations and units near counterfactual (unrealized) candidate locations, which differs from current empirical practice. I derive design-based standard errors that are straightforward to compute. For observational data, I propose machine learning methods to find counterfactual candidate locations when observable characteristics, rather than potential outcomes, determine treatment probabilities. To accommodate methods for high-dimensional data in the theory, I extend a double machine learning result to the design-based framework with spatial correlations. I apply the proposed methods to study the causal effects of grocery stores on foot traffic to nearby businesses during COVID-19 shelter-in-place policies, finding a large positive effect at very short distances, with no effect at larger distances.