We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| amber0309/ANM-MM | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| hsic_gam | Ran | amber0309/ANM-MM/HSIC.py code served (permissive licence) · get_code("92ec0d174432dfc8") |
| rbf_dot | Ran | amber0309/ANM-MM/HSIC.py code served (permissive licence) · get_code("3cf7414f45e07316") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model. In practice, however, observations are often collected from multiple sources with heterogeneous causal models due to certain uncontrollable factors, which renders causal analysis results obtained by a single model skeptical. In this paper, we generalize the Additive Noise Model (ANM) to a mixture model, which consists of a finite number of ANMs, and provide the condition of its causal identifiability. To conduct model estimation, we propose Gaussian Process Partially Observable Model (GPPOM), and incorporate independence enforcement into it to learn latent parameter associated with each observation. Causal inference and clustering according to the underlying generating mechanisms of the mixture model are addressed in this work. Experiments on synthetic and real data demonstrate the effectiveness of our proposed approach.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1809.08568")
get_code_for_paper("1809.08568")
have("1809.08568")
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