Local Causal Discovery of Direct Causes and Effects
Authors: Tian Gao, Qiang Ji
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show both theoretically and experimentally that the proposed local causal discovery algorithm can obtain the comparable identification accuracy as global methods but significantly improve their efficiency, often by more than one order of magnitude. We use benchmark causal learning datasets to evaluate the accuracy and efficiency of CMB with four other causal discovery algorithms discussed: P-C, GS, MMHC, CS, and the local causal discovery algorithm LCD2 [7]. |
| Researcher Affiliation | Academia | Tian Gao Qiang Ji Department of ECSE Rensselaer Polytechnic Institute, Troy, NY 12180 {gaot, jiq}@rpi.edu |
| Pseudocode | Yes | Algorithm 1 Causal Markov Blanket Discovery Algorithm; Algorithm 2 Causal Search Subroutine |
| Open Source Code | No | The paper mentions implementing algorithms in MATLAB but does not provide any explicit statement about releasing the source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We use benchmark causal learning datasets to evaluate the accuracy and efficiency of CMB with four other causal discovery algorithms discussed: P-C, GS, MMHC, CS, and the local causal discovery algorithm LCD2 [7]. Due to page limit, we show the results of the causal algorithms on four medium-to-large datasets: ALARM, ALARM3, CHILD3, and INSUR3. |
| Dataset Splits | No | The paper states 'We use 1000 data samples for all datasets' but does not specify any training, validation, or test splits (e.g., percentages or absolute counts) required for reproducibility. |
| Hardware Specification | Yes | We implement GS, CS, and the proposed CMB algorithms in MATLAB on a machine with 2.66GHz CPU and 24GB memory. |
| Software Dependencies | No | The paper mentions 'MATLAB' and 'HITON-MB discovery algorithm' but does not provide specific version numbers for any software components, which is necessary for a reproducible description of dependencies. |
| Experiment Setup | Yes | We use 1000 data samples for all datasets. We also use mutual-information-based conditional independence tests with a standard significance level of 0.02 for all the datasets without worrying about parameter tuning. |