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.

翻译：主要基于物理模型的证据已证实盐水注入是诱发地震的主要成因。为补充物理模型，统计/机器学习（ML）模型被设计用于从观测数据中度量关联性，既可采用参数回归模型，也可使用更灵活的ML模型。然而，参数统计显著性或模型预测能力通常难以被解释为因果关系的证据。我们采用基于潜在结果视角的因果推断框架，明确定义因果效应的内涵，并阐明恢复无偏因果效应估计所需的必要识别条件。特别地，我们通过模拟研究阐明时变混杂因素对观测性纵向地球科学数据的潜在威胁，并借鉴因果推断文献中成熟的纵向分析方法，估算了2013年至2016年期间废水注入对德克萨斯州中北部沃斯堡盆地地震活动的影响。