Kristian Agasøster Haaga
Kristian Agasøster Haaga
Zan et al. (2022) a new CMI estimator for mixed data and introduces the local adaptive permutation test, a variant of the `LocalPermutationTest`, which we already implement. The paper is...
Assaad et al. (2022) presents a new PC/FCI-like algorithm here: https://www.mdpi.com/1099-4300/24/8/1156. They also introduce a variant of the (conditional) mutual information that might be deserving of its own function.
The PCMCI/PCMCI+ algorithms are state-of-the-art when it comes to detection causal graphs from data. They should be included in the library. ## References - Runge, J., Nowack, P., Kretschmer, M.,...
Colombo et al. (2012) introduces the *really* fast causal inference (RFCI) algorithm. We should implement it. ## References Colombo, D., Maathuis, M. H., Kalisch, M., & Richardson, T. S. (2012)....
We should implement the [dual total correlation](https://en.wikipedia.org/wiki/Dual_total_correlation), which is a generalization of the mutual information.
[Kubkowski et al., 2021](https://www.jmlr.org/papers/volume22/19-600/19-600.pdf) paper introduces the short expansion of CMI. It also contains an associated conditional independence test. We should implement both the measure and the independence test. ##...
[Mukherjee et al. (2020)](http://proceedings.mlr.press/v115/mukherjee20a.html) introduces some classifier-based estimators of CMI based on neural networks. It would be nice to have these at some point. However, it would require some design...
Marx et al. (2021) introduces an adaptive-histogram based estimator for CMI. We should implement it. ## References Marx, A., Yang, L., & van Leeuwen, M. (2021). Estimating conditional mutual information...
Do the same as in #343 for `OCE` and `PC` algorithms with `infer_graph`
As mentioned in #341, independence testing can be very slow. The slow runtime is mostly due to intrinsic properties of the methods. However For example, in #341 it is mentioned...