Conditional Independence Testing using Generative Adversarial Networks
Authors: Alexis Bellot, Mihaela van der Schaar
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using synthetic simulations with high-dimensional data we demonstrate significant gains in power over competing methods. In addition, we illustrate the use of our test to discover causal markers of disease in genetic data. Sections 4 and 5 provide experiments on synthetic and real data respectively |
| Researcher Affiliation | Academia | Alexis Bellot1,2 Mihaela van der Schaar1,2,3 1University of Cambridge, 2The Alan Turing Institute, 3University of California Los Angeles |
| Pseudocode | Yes | Pseudo-code for the GCIT and full details on the implementation are given in Supplement D. |
| Open Source Code | Yes | An implementation of our test and tutorial are available at https://bitbucket.org/mvdschaar/mlforhealthlabpub/src/master/alg/gcit/. |
| Open Datasets | Yes | We use the subset of the CCLE data [1] relating to the drug PLX4720; it contains 474 cancer cell lines described by 466 genetic mutations. [1] Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A Margolin, Sungjoon Kim, Christopher J Wilson, Joseph Lehár, Gregory V Kryukov, Dmitriy Sonkin, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 483(7391):603, 2012. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits (e.g., percentages, sample counts, or citations to predefined splits) for the datasets used in its experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. It mentions synthetic simulations and experiments but no machine specifications. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. It mentions the use of "Generative Adversarial Networks" and "Energy-based generative neural networks" but no software versions like Python or PyTorch versions. |
| Experiment Setup | Yes | In practice, there will be a trade-off between the objectives of the discriminator and information network but we found that setting λ = 10 in our experiments achieved good performance. |