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Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable.
Equivalence and synthesis of causal models
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On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify all Causal Relations among N Variables
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Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs
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Joint causal inference from multiple contexts
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Invariant causal prediction for sequential data
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Approximate kernel-based conditional independence tests for fast non-parametric causal discovery
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A meta-transfer objective for learning to disentangle causal mechanisms
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