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..
Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
Authors: Jikai Jin, Vasilis Syrgkanis
NeurIPS 2024 | Venue PDF | 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 EMAIL Vasilis Syrgkanis Management Science and Engineering Stanford University Stanford, CA 94305 EMAIL |
| 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 pĪ²(x) exp |x|Ī² with parameters Ī²k = 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. |