Time-Varying Confounding Bias in Observational Geoscience with Application to Induced Seismicity

2025/10/18 by Yuchen Xiao, Corwin Zigler, Xiao, Yuchen +5
Computer Science · Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Geochemistry and Geologic Mapping #Spatial and Panel Data Analysis

paper · pdf · doi:10.48550/arxiv.2510.16360

openalex publication_date 2025/10/18 · openalex created_date 2025/10/22 · openalex updated_date 2026/07/28

Abstract

Evidence derived primarily from physical models has identified saltwater disposal as the dominant causal factor that contributes to induced seismicity. To complement physical models, statistical/machine learning (ML) models are designed to measure associations from observational data, either with parametric regression models or more flexible ML models. However, it is often difficult to interpret the statistical significance of a parameter or the predicative power of a model as evidence of causation. We adapt a causal inference framework with the potential outcomes perspective to explicitly define what we meant by causal effect and declare necessary identification conditions to recover unbiased causal effect estimates. In particular, we illustrate the threat of time-varying confounding in observational longitudinal geoscience data through simulations and adapt established statistical methods for longitudinal analysis from the causal interference literature to estimate the effect of wastewater disposal on earthquakes in the Fort-Worth Basin of North Central Texas from 2013 to 2016.

Citations

• Causal inference for process understanding in Earth sciences
• Bipartite Interference and Air Pollution Transport: Estimating Health Effects of Power Plant Interventions
• Causal inference methods for small non-randomized studies: Methods and\n an application in COVID-19
• A review of spatial causal inference methods for environmental and\n epidemiological applications
• A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications
• Generalized propensity score approach to causal inference with spatial interference
• Causal Inference in Geoscience and Remote Sensing from Observational Data
• Bipartite Causal Inference with Interference
• Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable
• A Theorem on the Manipulability of Redundant Serial Kinematic Chains
• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity

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