Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
Authors: Jikai Jin, Vasilis Syrgkanis
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on synthetic data and demonstrate the effectiveness of Li NGCRe L in the finite-sample regime. |
| Researcher Affiliation | Academia | Jikai Jin Institute for Computational and Mathematical Engineering Stanford University Stanford, CA 94305 jkjin@stanford.edu Vasilis Syrgkanis Management Science and Engineering Stanford University Stanford, CA 94305 vsyrgk@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Orthogonal-projections; Algorithm 2 Identify-Parents; Algorithm 3 Learn-Causal-Model |
| Open Source Code | No | Answer: [No] Justification: Code will be released after review. |
| Open Datasets | No | We generate the independent noise variables from generalized Gaussian distributions pβ(x) exp |x|β with parameters βk = 0.2k2, k = 1, 2, , d, multiplied by normalization constants to make their variances equal to 1. The ground-truth causal graph is generated by first fixing a total order of the vertices, say 1, 2, , d, then add directed edges i j(i < j) according to i.i.d. Bernoulli(p) distributions, where p (0, 1). |
| Dataset Splits | No | The paper discusses sample sizes for synthetic data but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | Answer: [No] Justification: The experiments do not require huge computational resources and can be run on a local computer. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | In our implementation of Algorithm 3, in each iteration we instead choose i / S that has the largest ratio between the first and second singular values of [q1, q2, , q K]. And in line 6 of Algorithm 2, we introduce a hyper-parameter tl such that the matrix [q1, q2, , q K] is considered to have rank rm 1 if its rm -th singular value is smaller than tl. |