- The proposed method is very similar in spirit to the approach in [10]. It seems that the method in [10] can also be equipped with scoring causal predictions and the interventional data. If otherwise, why [10] cannot use these side information? - The proposed method reduces the computation time drastically compared to [10] but this is achieved by reducing the search space to the ancestral graphs. This means that the output of ACI has less information compared to the output of [10] that has a richer search space, i.e., DAGs. This is the price that has been paid to gain a better performance. How much information of a DAG is encoded in its corresponding ancestral graph? - Second rule in Lemma 2, i.e., Eq (7) and the definition of minimal conditional dependence seem to be conflicting. Taking Zâ in this definition to be the empty set, we should have that x and y are independent given W, but Eq. (7) says otherwise.
- The proposed method is very similar in spirit to the approach in [10]. It seems that the method in [10] can also be equipped with scoring causal predictions and the interventional data. If otherwise, why [10] cannot use these side information?