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..
On the Parameter Identifiability of Partially Observed Linear Causal Models
Authors: Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on both synthetic and real-world datasets validate our identifiability theory and the effectiveness of the proposed method in the finitesample regime. |
| Researcher Affiliation | Collaboration | Xinshuai Dong1* Ignavier Ng1* Biwei Huang2 Yuewen Sun3 Songyao Jin3 Roberto Legaspi4 Peter Spirtes1 Kun Zhang1,3 1Carnegie Mellon University 2University of California San Diego 3Mohamed bin Zayed University of Artificial Intelligence 4KDDI Research |
| Pseudocode | No | The paper describes methods and objectives for parameter estimation but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code: https://github.com/dongxinshuai/scm-identify. |
| Open Datasets | Yes | In this section, we employ a famous psychometric dataset Big Five dataset https: //openpsychometrics.org/, to validate our method. |
| Dataset Splits | No | The paper mentions using different sample sizes (2k, 5k, 10k) for synthetic data and a dataset of 20,000 data points for real-world data, but it does not explicitly provide training/validation/test dataset splits needed to reproduce the experiments. |
| Hardware Specification | Yes | We conduct all the experiments with single Intel(R) Xeon(R) CPU E5-2470. |
| Software Dependencies | No | The paper states 'Our code is based on Python3.7 and Py Torch [37]' which provides a version for Python but not for PyTorch, and does not list multiple key software components with their specific versions. |
| Experiment Setup | Yes | Data is standardized and the optimization in Eqs. (4), (5), and (7) are solved by Adam [27], with a learning rate of 0.02. We will rely on 30 random starts and choose the one with the best likelihood. |