NO TEARS low rank (gCastle)

Module name

gcastle_notears_low_rank

Package

gCastle

Version

1.0.3

Language

Python

Docs

here

Paper

Fang et al.[1]

Graph type

DAG

MCMC

No

Edge constraints

No

Data type

C

Data missingness

Intervention type

Docker

bpimages/gcastle:1.0.3

NO TEARS low rank

Adapting NOTEARS for large problems with low-rank causal graphs.

Example

Config file: gcastle.json

Command:

snakemake --cores all --use-apptainer --configfile config/gcastle.json

Fig. 50 shows the pattern graph’s FP/P vs. TP/P benchmark results for 12 gCastle algorithms (and comparison with BOSS (TETRAD) and Iterative MCMC (BiDAG)). The benchmark is based on 5 datasets corresponding to 5 realisations of a 20-variable random Gaussian SEM with Erdős-Rényi structure (expected degree 4, max parents 5). Each dataset contains 300 standardized samples. The SEM parameters are uniformly sampled from [0.25, 1].

FP/P vs. TP/P for gCastle algorithms

Fig. 47 FP/P vs. TP/P for gCastle algorithms.

Example JSON

[
  {
    "id": "gcastle_notears_low_rank",
    "rank": 15,
    "w_init": null,
    "max_iter": 15,
    "h_tol": "1e-6",
    "rho_max": "1e+20",
    "w_threshold": 0.3,
    "timeout": null
  }
]