Comments (3)
Hi, thanks so much for your great suggestion. The reference has been added to the page.
from causal-learn.
Thank you for your quick reply.
I looked through the referenced paper and compared it to the code in the causal-learn repo. I have the impression that the description and the code do not really match. Also the code of the original R package and the causal-learn implementation seem to be quite different. Can you comment on that?
from causal-learn.
Hi, Stephan. Thanks for your question.
We didn't follow their code exactly. Our implementation is a standard autoregressive model with lasso penalties. This paper might also give you some ideas on the implementation. Equation (3) in this paper is the objective of our implemented algorithm. The optimisation procedure might be different from the paper. Also, we used cross validation to select the L1 penalty weight.
from causal-learn.
Related Issues (20)
- [RFC, META-ISSUE] Complete continuous integration (CI) for unit-testing, documentation, and test coverage HOT 4
- QUESTION: To get DAG/Graph from FCM-based methods HOT 1
- Add Error Handling for cit.py line 165/166 HOT 3
- Derivation of the Fisher-z independence test HOT 1
- the direct of two nodes in adjacency matrix which is result of ICALiNGAM HOT 3
- Passing required domain knowledge using add_required_by_node HOT 3
- GeneralGraph.subgraph bug HOT 1
- math domain error HOT 6
- I could add cluster background knowledge HOT 2
- p-values for edges using FCI HOT 1
- operands could not be broadcast together with shapes error with GES HOT 1
- PC algorithm graph matrix syntax
- GES graph node names issue HOT 1
- PC algorithm logic HOT 2
- Generalized score with mixed data HOT 1
- Using background knowledge makes FCI algorithm slower HOT 4
- Using scoring function 'local_score_marginal_multi' on ges() function gives error HOT 5
- PC algorithm and Meek rules HOT 9
- FAS does not use required edges anymore HOT 3
- Question: Clarification on Edge properties from FCI algo HOT 4
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