A polynomial-time algorithm for learning nonparametric causal graphs
Authors: Ming Gao, Yi Ding, Bryon Aragam
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we compare the proposed algorithm to existing approaches in a simulation study. Our simulation results can be summarized as follows: When implemented using generalized additive models [19], our method outperforms most state-of-the-art methods, particularly on denser graphs with hub nodes. |
| Researcher Affiliation | Academia | Ming Gao University of Chicago minggao@uchicago.edu Yi Ding University of Chicago dingy@uchicago.edu Bryon Aragam University of Chicago bryon@chicagobooth.edu |
| Pseudocode | Yes | Algorithm 1 Population algorithm for learning nonparametric DAGs. Algorithm 2 NPVAR algorithm. |
| Open Source Code | Yes | Code implementing the NPVAR algorithm is publicly available at https://github.com/Ming Gao97/ NPVAR. |
| Open Datasets | No | The paper describes generating synthetic data based on specified Graph types (Markov chains, Erdös-Rényi graphs, scale-free graphs) and Probability models (additive GPs, non-additive GPs, sine model, GLM). It does not use pre-existing publicly available datasets or provide links to them. Therefore, there is no concrete access information for a publicly available dataset. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It mentions "Randomly split the n samples in half" in Algorithm 2, which is an internal sample splitting technique for estimation, not a formal dataset partitioning for reproducibility of overall experimental results. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using "generalized additive models (GAMs)" for implementation but does not specify any software names with version numbers (e.g., Python, PyTorch, specific GAMs library with version). |
| Experiment Setup | No | The paper describes the graph types and probability models used for simulation studies but does not provide specific hyperparameter values or detailed system-level training settings in the main text. It states: "Full details of the implementations used as well as additional experiments can be found in the supplement." |