GIES (pcalg)

Module name	pcalg_gies
Package	pcalg
Version	2.7-8
Language	R
Docs	here
Paper	Hauser and Bühlmann[1]
Graph type	CPDAG
MCMC	No
Edge constraints	Yes
Data type	C
Data missingness
Intervention type	hard
Docker	bpimages/pcalg:2.7-8

Greedy Interventional Equivalence Search

Abstract: The investigation of directed acyclic graphs (DAGs) encoding the same Markov property, that is the same conditional independence relations of multivariate observational distributions, has a long tradition; many algorithms exist for model selection and structure learning in Markov equivalence classes. In this paper, we extend the notion of Markov equivalence of DAGs to the case of interventional distributions arising from multiple intervention experiments. We show that under reasonable assumptions on the intervention experiments, interventional Markov equivalence defines a finer partitioning of DAGs than observational Markov equivalence and hence improves the identifiability of causal models. We give a graph theoretic criterion for two DAGs being Markov equivalent under interventions and show that each interventional Markov equivalence class can, analogously to the observational case, be uniquely represented by a chain graph called interventional essential graph (also known as CPDAG in the observational case). These are key insights for deriving a generalization of the Greedy Equivalence Search algorithm aimed at structure learning from interventional data. This new algorithm is evaluated in a simulation study.

Some fields described

edgeConstraints Name of the JSON file containing background knowledge

Example JSON

[
  {
    "id": "gies",
    "maxDegree": null,
    "iterate": true,
    "verbose": false,
    "timeout": null,
    "edgeConstraints": "edgeConstraints.json"
  }
]