# Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matching

arXiv: https://arxiv.org/abs/1610.07583 · HTML: https://mlsanity.com/paper/1610.07583/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2016.

## Built on by (most cited first, 7 of 7)

- [EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments](https://mlsanity.com/paper/2607.05155/) (2026)
- [Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning](https://mlsanity.com/paper/2405.03342/) (2024)
- [SpaCE: The Spatial Confounding Environment](https://mlsanity.com/paper/2312.00710/) (2023)
- [Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference](https://mlsanity.com/paper/2510.08762/) (2025)
- [Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences](https://mlsanity.com/paper/2407.17910/) (2024)
- [Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning](https://mlsanity.com/paper/2509.18484/) (2025)
- [Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2609.36046/) (2026)

## Abstract (arXiv)

Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding. In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units may serve as a useful proxy for unmeasured confounding that varies according to a spatial pattern. We develop a new method, termed Distance Adjusted Propensity Score Matching (DAPSm) that incorporates information on units' spatial proximity into a propensity score matching procedure. We show that DAPSm can adjust for both observed and some forms of unobserved confounding and evaluate its performance relative to several other reasonable alternatives for incorporating spatial information into propensity score adjustment. The method is motivated by and applied to a comparative effectiveness investigation of power plant emission reduction technologies designed to reduce population exposure to ambient ozone pollution. Ultimately, DAPSm provides a framework for augmenting a "standard" propensity score analysis with information on spatial proximity and provides a transparent and principled way to assess the relative trade offs of prioritizing observed confounding adjustment versus spatial proximity adjustment.