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Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems.
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Deep structural causal models for tractable counterfactual inference
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Nested counterfactual identification from arbitrary surrogate experiments
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On pearl’s hierarchy and the foundations of causal inference
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Causal identification under markov equivalence: Calculus, algorithm, and completeness
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Partial counterfactual identification from observational and experimental data
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