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Found 5 papers, 2 papers with code
Entropic Causal Inference: Identifiability and Finite Sample Results
This unobserved randomness is measured by the entropy of the exogenous variable in the underlying structural causal model, which governs the causal relation between the observed variables.
Active Structure Learning of Causal DAGs via Directed Clique Trees
Most existing works focus on \textit{worst-case} or \textit{average-case} lower bounds for the number of interventions required to orient a DAG.
Active Structure Learning of Causal DAGs via Directed Clique Tree
Most existing works focus on worst-case or average-case lower bounds for the number of interventions required to orient a DAG.
Sample Efficient Active Learning of Causal Trees
We consider the problem of experimental design for learning causal graphs that have a tree structure.
Size of Interventional Markov Equivalence Classes in Random DAG Models
For constant density, we show that the expected $\log$ observational MEC size asymptotically (in the number of vertices) approaches a constant.
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