Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference
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连接预测与归因：利用同化因果推断识别前向与后向因果影响范围
- 研究问题
- 结合观测与动力学模型时，如何确定原因的影响持续多久，以及观测到的结果由何时开始的触发因素引起，并刻画随时间变化的范围？
- 方法与思路
- 作者利用贝叶斯数据同化中的不确定性减少，定义逐时点前向与后向因果影响范围，提出无需经验阈值的度量及高效近似算法。
- 材料陈述的贡献
- 摘要称，研究给出两类影响范围的数学表述和计算方法，并通过非线性动力系统模拟研究临界转变、干扰变量与大气阻塞机制。
- 阅读关注点
- 应核对因果影响范围与不确定性减少的关系、动力模型的作用，以及所谓稳健度量、闭式表达和近似算法各自成立的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核验。摘要未说明完整因果识别与模型误设条件，不能据此确认其政策应用有效性。
原文摘要
Causal inference identifies cause-and-effect relationships between variables. While traditional approaches rely on data to reveal causal links, a recently developed method, assimilative causal inference (ACI), integrates observations with dynamical models. It utilizes Bayesian data assimilation to trace causes back from observed effects by quantifying the reduction in uncertainty. ACI advances the detection of instantaneous causal relationships and the intermittent reversal of causal roles over time. Beyond identifying causal connections, an equally important challenge is determining the associated causal influence range (CIR), indicating when causal influences emerged and for how long they persist. In this paper, ACI is employed to develop mathematically rigorous formulations of both forward and backward CIRs at each time. The forward CIR quantifies the temporal impact of a cause, while the backward CIR traces the onset of triggers for an observed effect, thus characterizing causal predictability and attribution of outcomes at each transient phase, respectively. Objective and robust metrics for both CIRs are introduced, eliminating the need for empirical thresholds. Computationally efficient approximation algorithms to compute CIRs are developed, which facilitate the use of closed-form expressions for a broad class of nonlinear dynamical systems. Numerical simulations demonstrate how this forward and backward CIR framework provides new possibilities for probing complex dynamical systems. It advances the study of bifurcation-driven and noise-induced tipping points in Earth systems, investigates the impact from resolving the interfering variables when determining the influence ranges, and elucidates atmospheric blocking mechanisms in the equatorial region. These results have direct implications for science, policy, and decision-making.