24 citations · 24 across the 3 of their papers we have counts for
4 papers · 1 filter
A unified framework for estimating direct causal effect under spatial confounding and interference, with the R package spaci
Isqeel Ogunsola, Olatunji Johnson
Estimating causal effects is challenging with observational data due to the lack of exchangeability, and is further complicated with spatial data. In real-life applications such as…
Decoupling Distance and Networks: Hybrid Graph Attention-Geostatistical Methods for Spatio-temporal Risk Mapping
Toba Temitope Bamidele, Ezra Gayawan, Femi Barnabas Adebola +1
Accurate spatial prediction and rigorous uncertainty quantification are central to modern spatial epidemiology and environmental risk analysis. We introduce a statistically princip…
Disentangling spatial interference and spatial confounding biases in causal inference
Isqeel Ogunsola, Olatunji Johnson
Spatial interference and spatial confounding are two major issues inhibiting precise causal estimates when dealing with observational spatial data. Moreover, the definition and int…
A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data
Olatunji Johnson, Peter Diggle, Emanuele Giorgi
In this paper, we develop a computationally efficient discrete approximation to log-Gaussian Cox process (LGCP) models for the analysis of spatially aggregated disease count data.…