Identifying General Mechanism Shifts in Linear Causal Representations
Authors: Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our results on both synthetic experiments as well as an interesting psychometric dataset. |
| Researcher Affiliation | Academia | Department of Statistics and Data Sciences, University of Texas at Austin Booth School of Business, University of Chicago Machine Learning Department, Carnegie Mellon University Institute of Science and Technology Austria |
| Pseudocode | Yes | Algorithm 1 i LCS: Identifying Latent Causal Mechanisms Shifts |
| Open Source Code | Yes | The code can be found at https://github.com/Tianyu Codings/i LCS. |
| Open Datasets | Yes | The data can be downloaded via the link: https://www.kaggle.com/datasets/ lucasgreenwell/ocean-five-factor-personality-test-responses/data. |
| Dataset Splits | No | The paper does not explicitly specify training, validation, and test dataset splits (e.g., percentages or sample counts) for model training and evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using ICA but does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | The parameter α is set to 0.2 for d 10 and 0.5 for higher dimensions, reflecting the increased complexity in estimating larger dimensional latent graphs and thus necessitating a higher tolerance for L1 norm differences in detecting shifted nodes. For each graph, the weights are independently sampled from Unif [0.25, 1] and the diagonal entries of Ωfrom Unif[2, 4]. In each environment k, 15% of the nodes are randomly selected for shifting. The new weights A(k) i for the shifted node i, and the new entries of Ω(k), specifically Ω(k) ii , are independently sampled from Unif[6, 8]. The mixing function G is independently generated from Unif[ 0.25, 0.25]. |