Interventional Causal Discovery in a Mixture of DAGs
Authors: Burak Varıcı, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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]. |