Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Nonlinear Causal Discovery with Latent Confounders
Authors: David Kaltenpoth, Jilles Vreeken
ICML 2023 | Venue PDF | 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. |