We lifted 6 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| quangdzuytran/ROCHE | canonical | 4 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| HSIC | Ran | quangdzuytran/ROCHE/causa/hsic_torch.py code served (permissive licence) · get_code("97c4e23c2181e7fd") |
| build_het_network | Ran | quangdzuytran/ROCHE/causa/roche.py code served (permissive licence) · get_code("268c670e12c7fb1b") |
| gaussian_grammat | Ran | quangdzuytran/ROCHE/causa/hsic_torch.py code served (permissive licence) · get_code("bceca8207f63f53b") |
| loss_func | Ran | quangdzuytran/ROCHE/causa/roche.py code served (permissive licence) · get_code("4a43f04570f2e3bd") |
| centering | Not yet run | quangdzuytran/ROCHE/causa/hsic_torch.py code served (permissive licence) · get_code("a8c3780224cc4a67") |
| map_optimization | Not yet run | quangdzuytran/ROCHE/causa/roche.py code served (permissive licence) · get_code("fa635d3cdd4d9a5e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Distinguishing the cause and effect from bivariate observational data is the foundational problem that finds applications in many scientific disciplines. One solution to this problem is assuming that cause and effect are generated from a structural causal model, enabling identification of the causal direction after estimating the model in each direction. The heteroscedastic noise model is a type of structural causal model where the cause can contribute to both the mean and variance of the noise. Current methods for estimating heteroscedastic noise models choose the Gaussian likelihood as the optimization objective which can be suboptimal and unstable when the data has a non-Gaussian distribution. To address this limitation, we propose a novel approach to estimating this model with Student's $t$-distribution, which is known for its robustness in accounting for sampling variability with smaller sample sizes and extreme values without significantly altering the overall distribution shape. This adaptability is beneficial for capturing the parameters of the noise distribution in heteroscedastic noise models. Our empirical evaluations demonstrate that our estimators are more robust and achieve better overall performance across synthetic and real benchmarks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.10102")
get_code_for_paper("2312.10102")
have("2312.10102")
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