<ul><li>Counterfactual reasoning is often seen as the 'holy grail' of causal learning, but its reliability in real-world complex settings is largely unexplored.</li><li>This work investigates the limitations of counterfactual reasoning within the framework of Structural Causal Models.</li><li>Realistic assumptions, such as model uncertainty and chaotic dynamics, can lead to counterintuitive outcomes.</li><li>This study encourages caution when applying counterfactual reasoning in chaotic and uncertain situations.</li></ul>

When Counterfactual Reasoning Fails: Chaos and Real-World Complexity

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