GSP (CausalDAG)

Module name	causaldag_gsp
Package	CausalDAG
Version	0.1a163
Language	Python
Docs	here
Paper	Solus et al.[1], Chandler Squires[2], Raskutti and Uhler[3]
Graph type	DAG
MCMC	No
Edge constraints	No
Data type	C
Data missingness
Intervention type
Docker	bpimages/causaldag:0.1a163

Greedy sparsest permutation

Example JSON

[
  {
    "id": "gsp",
    "nruns": 5,
    "depth": 4,
    "verbose": true,
    "initial_undirected": "threshold",
    "use_lowest": true,
    "max_iters": "inf",
    "factor": 2,
    "progress_bar": false,
    "summarize": false,
    "alpha": [
      0.05,
      0.001
    ],
    "invert": true,
    "timeout": null
  }
]

[1]

L Solus, Y Wang, and C Uhler. Consistency guarantees for greedy permutation-based causal inference algorithms. Biometrika, 108(4):795–814, 01 2021. URL: https://doi.org/10.1093/biomet/asaa104.

[2]

Chandler Squires. causaldag: creation, manipulation, and learning of causal models. 2018. URL: https://github.com/uhlerlab/causaldag.

[3]

Garvesh Raskutti and Caroline Uhler. Learning directed acyclic graph models based on sparsest permutations. Stat, 7(1):e183, 2018. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/sta4.183.