Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable to spurious associations and thus, they are not guaranteed to extract causally-relevant insights.
Jun 7, 2023
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce.
Jun 7, 2023 · We demonstrate both theoretically and empirically that our method can identify the causal parents of a variable of interest in polynomial time.
In this work, we build on ideas from the invariant causal prediction literature to propose Invariant Causal Set Covering Machines, an extension of the classical ...
Invariant Causal Set Covering Machine. Link to the paper : https://arxiv.org/pdf/2306.04777.pdf. To run experimentation on simulated data: python experiment.py.
We demonstrate both theoretically and empirically that our method can identify the causal parents of a variable of interest in polynomial time. READ FULL TEXT.
Pascal Germain. Latest. Invariant Causal Set Covering Machines. Powered by the Academic theme for Hugo. Cite. �. Copy Download.
@inproceedings{godon2023icscm, title = {Invariant Causal Set Covering Machines}, author = {Thibaud Godon and Baptiste Bauvin and Pascal Germain and Jacques ...
The i.i.d. setting is theoretically well understood and yields remarkable predictive accuracy in problems such as image classification, speech recognition and ...
Missing: Covering | Show results with:Covering
Regularizing Adversarial Imitation Learning Using Causal Invariance ( Poster ) > link ... Invariant Causal Set Covering Machines ( Poster ) > link · Link. Thibaud ...