Nonlinear Causal Discovery with Latent Confounders
Authors: David Kaltenpoth, Jilles Vreeken
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically we show that it outperforms other state-of-the-art methods for causal discovery under latent confounding on synthetic and real-world data. |
| Researcher Affiliation | Academia | CISPA Helmholtz Center for Information Security, Germany. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All code and results can be found on the authors website.1 1https://eda.rg.cispa.io/prj/fanta/ We make all code and results available in the supplement. |
| Open Datasets | Yes | We evaluate it on the REGED dataset (Guyon et al., 2008)... Sachs dataset (Sachs et al., 2005). |
| Dataset Splits | No | The paper describes the data generation process for synthetic data and mentions sample sizes, but does not specify explicit training, validation, or test splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | We implemented NOCADILAC using Tensorflow (Abadi et al., 2016) and perform optimization using Adam (Kingma & Ba, 2014). The paper mentions software names but does not provide specific version numbers for reproducibility. |
| Experiment Setup | No | The paper mentions using Adam for optimization but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations. |