Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
Authors: Junsouk Choi, Robert Chapkin, Yang Ni
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the utility of the proposed ZIPBN in causal discoveries for zero-inflated count data by simulation studies with comparison to alternative Bayesian network methods. Additionally, real single-cell RNA-sequencing data with known causal relationships will be used to assess the capability of ZIPBN for discovering causal relationships in real-world problems. |
| Researcher Affiliation | Academia | Junsouk Choi Department of Statistics Texas A&M University College Station, TX 77843 jchoi@stat.tamu.edu Robert Chapkin Department of Nutrition Texas A&M University College Station, TX 77843 r-chapkin@tamu.edu Yang Ni Department of Statistics Texas A&M University College Station, TX 77843 yni@stat.tamu.edu |
| Pseudocode | Yes | Algorithm 1 Parallel-Tempered MCMC for ZIPBN |
| Open Source Code | No | The paper states 'The code implementing the MCMC is available in the Supplementary Material.' This does not explicitly state it is open-source or provide a direct public link, which is required by the prompt's criteria. |
| Open Datasets | Yes | Additionally, real single-cell RNA-sequencing data with known causal relationships will be used to assess the capability of ZIPBN for discovering causal relationships in real-world problems. |
| Dataset Splits | No | The paper mentions 'simulated data under different samples sizes n {250, 500, 1000}' and 'retained 479 pairs for causal validation', but does not provide explicit train/validation/test splits with percentages or sample counts for reproducibility. |
| Hardware Specification | Yes | The CPU time was 1.7 hours on an i9-9880H 2.3GHz CPU. |
| Software Dependencies | No | The paper mentions 'R package Seurat (Butler et al., 2018)' but does not provide specific version numbers for R, Seurat, or any other software components. |
| Experiment Setup | Yes | For the proposed ZIPBN, we used non-informative prior by setting the hyperparameters to be (aτ, bτ) = (0.01, 0.01) and (aρ, bρ) = (0.5, 0.5)... We ran M = 10 parallel chains for 3, 000 iterations, of which the first 1, 500 iterations were discarded as burn-in. The temperatures were chosen uniformly between 0 and 1 on the log-scale, i.e., log(Tm) = (m 1)/9 for m = 1, . . . , 10. The swapping probability ps was chosen to be 10%. |