Market generators using deep generative models have shown promise for synthetic financial data generation, but existing approaches lack causal reasoning capabilities essential for counterfactual analysis and risk assessment. We propose a Time-series Neural Causal Model VAE (TNCM-VAE) that combines variational autoencoders with structural causal models to generate counterfactual financial time series while preserving both temporal dependencies and causal relationships. Our approach enforces causal constraints through directed acyclic graphs in the decoder architecture and employs the causal Wasserstein distance for training. We validate our method on synthetic autoregressive models inspired by the Ornstein-Uhlenbeck process, demonstrating superior performance in counterfactual probability estimation with L1 distances as low as 0.03-0.10 compared to ground truth. The model enables financial stress testing, scenario analysis, and enhanced backtesting by generating plausible counterfactual market trajectories that respect underlying causal mechanisms.

翻译：基于深度生成模型的市场生成器在合成金融数据生成方面展现出潜力，但现有方法缺乏对反事实分析和风险评估至关重要的因果推理能力。我们提出一种结合变分自编码器与结构因果模型的时间序列神经因果模型VAE（TNCM-VAE），能在保持时序依赖性与因果关系的同时生成反事实金融时间序列。该方法通过在解码器架构中引入有向无环图强制因果约束，并采用因果Wasserstein距离进行训练。我们在受Ornstein-Uhlenbeck过程启发的合成自回归模型上验证了该方法，其反事实概率估计性能优异，与真实值的L1距离可低至0.03-0.10。该模型通过生成符合底层因果机制的合理反事实市场轨迹，能够实现金融压力测试、情景分析和增强的回测验证。