CIR-ACTIVA能对信用违约互换利差做因果冲击预测，短周期准确性领先观测基线，金融风险场景适用。
该论文针对多元CIR过程提出摊销式干预预测模型CIR-ACTIVA，用于估计分布因果效应并预测外部冲击下的多时间跨度响应。作者设计了一个因果多元CIR数据生成过程，提供成对的观测与干预真实值，并在CDS利差上完成校准测试。在合成基准上，CIR-ACTIVA相比观测式与摊销式因果推断基线，在联合分布因果选择性和分时校准方面均领先。优势集中在短预测跨度，且干预律随时间变化时依然保持选择性。
Amortized Interventional Forecasting for Multivariate CIR Processes
Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from short rates to credit default swap (CDS) spreads. Yet CIR models capture only \emph{correlated} co-movement, not \emph{causal} influence between series, so they cannot answer the system's response when one series is externally shocked, which observational conditionals confound with historical co-movement. We make two contributions. First, an amortized model for distributional causal effect estimation that frames trajectories as time-stamped observations and predicts the calibrated multi-horizon shock response without retraining per scenario. Second, a causal multivariate CIR data-generating process that supplies the paired observational and interventional ground truth that real markets cannot. We instantiate and calibrate the framework on CDS spreads as a testbed. CIR-ACTIVA's validity is established on synthetic ground truth, independent of how well the simulator matches reality, while practical grounding is assessed by backtesting the generated traces against real CDS data. Against observational and amortized causal-inference baselines, CIR-ACTIVA leads on both causal selectivity in the joint distribution and horizon-resolved calibration, retaining its selectivity once the interventional law varies over the horizon, with gains concentrating at short horizons. This opens up a class of what-if queries on coupled spread systems, CDS stress testing among them, that observational forecasters cannot answer.