We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| psanch21/VACA | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| ELBO | Ran | psanch21/VACA/models/_density_estimators.py code served (permissive licence) · get_code("c8d922f4dc2cba7d") |
| IWAE | Ran | psanch21/VACA/models/_density_estimators.py code served (permissive licence) · get_code("b1bca1bbdeac09e3") |
| IWAE_dreg | Ran | psanch21/VACA/models/_density_estimators.py code served (permissive licence) · get_code("9d91917f85907a4f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available. Without making any parametric assumptions, VACA mimics the necessary properties of a Structural Causal Model (SCM) to provide a flexible and practical framework for approximating interventions (do-operator) and abduction-action-prediction steps. As a result, and as shown by our empirical results, VACA accurately approximates the interventional and counterfactual distributions on diverse SCMs. Finally, we apply VACA to evaluate counterfactual fairness in fair classification problems, as well as to learn fair classifiers without compromising performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.14690")
get_code_for_paper("2110.14690")
have("2110.14690")
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