Counterfactual reasoning, a cornerstone of human cognition and decision-making, is often seen as the 'holy grail' of causal learning, with applications ranging from interpreting machine learning models to promoting algorithmic fairness. While counterfactual reasoning has been extensively studied in contexts where the underlying causal model is well-defined, real-world causal modeling is often hindered by model and parameter uncertainty, observational noise, and chaotic behavior. The reliability of counterfactual analysis in such settings remains largely unexplored. In this work, we investigate the limitations of counterfactual reasoning within the framework of Structural Causal Models. Specifically, we empirically investigate \emph{counterfactual sequence estimation} and highlight cases where it becomes increasingly unreliable. We find that realistic assumptions, such as low degrees of model uncertainty or chaotic dynamics, can result in counterintuitive outcomes, including dramatic deviations between predicted and true counterfactual trajectories. This work urges caution when applying counterfactual reasoning in settings characterized by chaos and uncertainty. Furthermore, it raises the question of whether certain systems may pose fundamental limitations on the ability to answer counterfactual questions about their behavior.

翻译：反事实推理作为人类认知与决策的基石，常被视为因果学习的“圣杯”，其应用范围涵盖从解释机器学习模型到促进算法公平的广泛领域。尽管反事实推理在基础因果模型明确定义的情境中已得到深入研究，但现实世界的因果建模常受模型与参数不确定性、观测噪声及混沌行为所制约。在此类情境下反事实分析的可靠性仍很大程度上未被探索。本研究在结构因果模型框架内系统探究反事实推理的局限性。具体而言，我们通过实证研究考察**反事实序列估计**，并重点揭示其可靠性显著降低的案例。研究发现，诸如低程度模型不确定性或混沌动力学等现实假设，可能导致反直觉的结果，包括预测反事实轨迹与真实轨迹之间的显著偏差。本研究成果警示在混沌与不确定性特征显著的情境中应用反事实推理需持审慎态度。此外，研究进一步提出深刻问题：某些系统是否可能对其行为反事实问题的解答能力存在根本性限制。