Characterization and Learning of Causal Graphs with Small Conditioning Sets
Authors: Murat Kocaoglu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct synthetic, and semi-synthetic experiments to demonstrate that the k-PC algorithm enables more robust causal discovery in the small sample regime compared to the baseline algorithms. |
| Researcher Affiliation | Academia | Murat Kocaoglu School of Electrical and Computer Engineering Purdue University mkocaoglu@purdue.edu |
| Pseudocode | Yes | Algorithm 1 k-PC Algorithm [...] Algorithm 2 FCI_Orient |
| Open Source Code | Yes | The Python code is provided at https://github.com/CausalML-Lab/kPC. |
| Open Datasets | Yes | We test our algorithm on the semi-synthetic Asia dataset from bnlearn repository and compare it with PC algorithm. [...] We randomly sample DAGs using the pyAgrum package. |
| Dataset Splits | No | The paper mentions generating multiple datasets per model instance but does not specify a formal validation set or explicit training/validation/test splits, percentages, or absolute sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions 'causal-learn package', 'pyAgrum package', and 'pcalg package in R' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For each DAG, conditional probability tables are independently and uniformly randomly filled from the corresponding probability simplex. [...] For each DAG, a linear SCM is sampled as follows: Each coefficient is chosen randomly in the range [-3, 3]. Exogenous noise terms are jointly independent unit Gaussian. |