Thuật ngữ AI
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Causal Markov Condition
Fundamental principle stating that a variable is independent of its non-descendants conditional on its direct parents in the corresponding causal graph.
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Back-door Criterion
Set of variables satisfying specific conditions that allow unbiased identification of the total causal effect of a treatment on an outcome by adjusting for confounding factors.
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SEM Model
Statistical framework modeling causal relationships between latent and observable variables through a system of structural equations representing underlying causal mechanisms.
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Faithfulness Assumption
Principle assuming that all conditional independencies in the data arise from the causal graph structure, without fortuitous statistical coincidences.
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