Linear Causal Representation Learning from Unknown Multi-node Interventions
Authors: Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Simulations We empirically assess the performance of the UMNI-CRL algorithm for recovering the latent DAG G and latent variables Z. |
| Researcher Affiliation | Collaboration | Burak Varıcı Carnegie Mellon University Emre Acartürk Rensselaer Polytechnic Institute Karthikeyan Shanmugam Google Deep Mind Ali Tajer Rensselaer Polytechnic Institute |
| Pseudocode | Yes | Algorithm 1 Unknown Multi-node Interventional (UMNI)-CRL |
| Open Source Code | Yes | The codebase for the experiments can be found at https://github.com/acarturk-e/umni-crl. |
| Open Datasets | No | The paper describes generating its own synthetic data for experiments ('To generate G, we use Erd os-Rényi model...', 'observed variables are generated as X = G Z'), and does not mention using a publicly available or open dataset with a specific link, DOI, or formal citation. |
| Dataset Splits | No | The paper states 'generate ns = 105 samples of Z from each environment' but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper states 'Experiments are run on a single commercial CPU' but does not provide specific hardware details such as exact CPU models, processor types with speeds, or memory amounts. |
| Software Dependencies | No | The paper mentions statistical models and general types of estimators for score functions but does not provide specific software names with version numbers, such as library or solver names. |
| Experiment Setup | Yes | For the causal models, we adopt linear structural equation models (SEMs) with Gaussian noise. The nonzero edge weights of the linear SEMs are sampled from Unif( [0.5, 1.5]), and the noise terms are zero-mean Gaussian variables with variances σ2 i sampled from Unif([0.5, 1.5])... We consider target dimensions d {10, 50}, generate ns = 105 samples of Z from each environment... we use a partial correlation test and set the significance level to α = 0.05... we set κ = 2 in the simulations in Section 5. |