Comments (5)
I also have the same issue when I use causallearn.search.ScoreBased.GES
. I guess it is caused by input data. I don't know what kind of requirements should be met. It would be good if developers could list requirements for input data.
from causal-learn.
do u think if it was a good idea to calculate pseudo inverse, if the inverse of the sub_corr_matrix gives error?
from causal-learn.
Yea, this is due to some violation of the data-generating process, e.g., violation of faithfulness. I don't know if any strategy exists to detect this given an observed dataset. The pseudo-inverse could be a good solution in practice, but we need to investigate deeper to see if that would introduce any issue with the asymptotic guarantee.
from causal-learn.
Perhaps adding some small random noises could help?
from causal-learn.
Yes and can you check two things:
a) distinct count per column
b) distinct count of identical rows
What I learned with repeated data, it does create singular matrix.
Also interested to learn!
from causal-learn.
Related Issues (20)
- does this codebase support some recent causal discovery methods like, notears, dag-gnn,and so on HOT 2
- A potential Bug in GES.py HOT 1
- Null Hypothesis in Conditoinal Independence Tests HOT 2
- How to construct a causallearn.graph.Graph object from an numpy array? HOT 2
- Boostrap Utilities HOT 1
- Background knowledge not used correctly? HOT 1
- Implementation for CCI? HOT 1
- How to estimate the causal effect? HOT 1
- Different results with Tetrad and causal-learn implementations. HOT 6
- numpy version HOT 1
- Understanding the FCI outputs (graph vs. printed edges) HOT 3
- Handling Data with Interventions HOT 4
- array must not contain infs or NaNs HOT 2
- use of deprecated `np.mat` HOT 3
- IndexError: tuple index out of range - From LocalScoreFunction.py HOT 1
- Key error FCI HOT 1
- Causal Discovery Algorithms For Time Series Data HOT 2
- Implementation of DAGMA
- Numpy overflow with discrete data HOT 3
- Potential Issue with get_sepset Function Failing to Identify Valid Separating Sets for Conditional Independence HOT 1
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from causal-learn.