Causal Component Analysis
Authors: Liang Wendong, Armin Kekić, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce a likelihood-based approach using normalizing flows to estimate both the unmixing function and the causal mechanisms, and demonstrate its effectiveness through extensive synthetic experiments in the Cau CA and ICA setting. |
| Researcher Affiliation | Academia | 1 Max Planck Institute for Intelligent Systems, Tübingen, Germany 2 ENS Paris-Saclay, Gif-sur-Yvette, France 3 University of Cambridge, United Kingdom |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/akekic/causal-component-analysis. |
| Open Datasets | No | The paper states: "Synthetic data-generating process. We first sample DAGs G..." and "We then sample observed mixtures from these latent CBNs, as described by eq. (2)." This indicates that the authors generated their own synthetic data and do not provide access to a publicly available dataset. |
| Dataset Splits | Yes | We train the model for 50-200 epochs with a batch size of 4096. The number of epochs was tuned manually for each type of experiment to ensure reliable convergence of the validation log probability. For each drawn latent CBN, we train three models with different initializations and select the model with the highest validation log probability at the end of training. |
| Hardware Specification | Yes | Each training run takes 2-8 hours on NVIDIA RTX-6000 gpus. |
| Software Dependencies | No | The paper mentions "ADAM optimizer" and "Neural Spline Flows" (which implies a deep learning framework), but it does not provide specific version numbers for these or other software libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use the ADAM optimizer [27] with cosine annealing learning rate scheduling, starting with a learning rate of 5 imes 10^-3 and ending with 1 imes 10^-7. We train the model for 50-200 epochs with a batch size of 4096. The number of epochs was tuned manually for each type of experiment to ensure reliable convergence of the validation log probability. |