ANMNonlinear (gCastle)

Module name

gcastle_anm

Package

gCastle

Version

1.0.3

Language

Python

Docs

here

Paper

Hoyer et al.[1]

Graph type

DAG

MCMC

No

Edge constraints

No

Data type

C, D

Data missingness

Intervention type

Docker

bpimages/gcastle:1.0.3

ANMNonlinear

Nonlinear causal discovery with additive noise models.

Example

Config file: gcastle_nonlinear.json

Command:

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

Fig. 48 and Fig. 49 show the pattern graph’s FP/P vs. TP/P benchmark results for gCastle algorithms, and comparison with BOSS (TETRAD) and Iterative MCMC (BiDAG), tested on nonlinear data. The benchmark is based on 5 datasets corresponding to 5 realisations of a 20-variable random nonlinear Gaussian SEM with Erdős-Rényi structure (expected degree 4, max parents 5). The nonlinear relationships are modeled using multi-layer perceptrons (MLP) and quadratic functions, see gcastle_iidsim. Each dataset contains 300 standardized samples. The SEM parameters are uniformly sampled from [0.25, 1].

FP/P vs. TP/P for gCastle algorithms on nonlinear MLP data

Fig. 24 FP/P vs. TP/P for gCastle algorithms on nonlinear MLP data.

FP/P vs. TP/P for gCastle algorithms on nonlinear quadratic data

Fig. 25 FP/P vs. TP/P for gCastle algorithms on nonlinear quadratic data.

Example JSON

[
  {
    "id": "gcastle_anm",
    "alpha": 0.05,
    "timeout": null
  }
]