most citedCausality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference

Omar Faruque, Pavan Raj Ravi, Jianwu Wang

Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference. Standard causal identification methods assume conditional e…

cs.LG2024

Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data

Francis Ndikum Nji, Omar Faruque, Mostafa Cham +2

Classifying subsets based on spatial and temporal features is crucial to the analysis of spatiotemporal data given the inherent spatial and temporal variability. Since no single cl…

cs.LG2024

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

Sahara Ali, Omar Faruque, Jianwu Wang

Spatial interference (SI) occurs when the treatment at one location affects the outcomes at other locations. Accounting for spatial interference in spatiotemporal settings poses fu…

physics.data-an2024★ 2 cited

Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods

Sahara Ali, Uzma Hasan, Xingyan Li +5

This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science. More specifically, the paper presents a…

cs.LG2024★ 1 cited

TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data

Omar Faruque, Sahara Ali, Xue Zheng +1

The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for…