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..
Interventional Causal Discovery in a Mixture of DAGs
Authors: Burak Varıcı, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of Algorithm 1 for estimating the true edges in a mixture of DAGs using synthetic data and investigate the need for interventions, the effect of the graph size, and the cyclic complexity. |
| Researcher Affiliation | Collaboration | Burak Varıcı Carnegie Mellon University Dmitriy A. Katz IBM Research Dennis Wei IBM Research Prasanna Sattigeri IBM Research Ali Tajer Rensselaer Polytechnic Institute |
| Pseudocode | Yes | Algorithm 1 Causal Discovery from Interventions on Mixture Models (CADIM) |
| Open Source Code | Yes | The codebase for the experiments can be found at https://github.com/bvarici/intervention-mixture-DAG. |
| Open Datasets | No | We use an Erd os-Rényi model G(n, p) with density p = 2/n to generate the component DAGs {Gℓ: ℓ [K]} for different values of nodes n and mixture components K. We adopt linear structural equation models (SEMs) with Gaussian noise for the causal models... |
| Dataset Splits | No | We look into the performance of Algorithm 1 under a varying number of nodes n [5, 30] for a mixture of K = 3 DAGs and using 5000 samples from each DAG. No explicit mention of train/validation/test splits is provided. |
| Hardware Specification | Yes | Experiments are run on a single commercial CPU. |
| Software Dependencies | No | The paper mentions using a “partial correlation test” but does not provide specific version numbers for software dependencies or libraries used. |
| Experiment Setup | Yes | We use an Erd os-Rényi model G(n, p) with density p = 2/n to generate the component DAGs... We adopt linear structural equation models (SEMs) with Gaussian noise for the causal models, in which the noise for node i is sampled from N(µi, σ2 i ) where µi is sampled uniformly in [ 1, 1] and σ2 i is sampled uniformly in [0.5, 1.5]. The edge weights are sampled uniformly in [0.25, 2]. |