Fast Restricted Causal Inference

07/13/2017
by   Mieczysław A. Kłopotek, et al.
0

Hidden variables are well known sources of disturbance when recovering belief networks from data based only on measurable variables. Hence models assuming existence of hidden variables are under development. This paper presents a new algorithm "accelerating" the known CI algorithm of Spirtes, Glymour and Scheines Spirtes:93. We prove that this algorithm does not produces (conditional) independencies not present in the data if statistical independence test is reliable. This result is to be considered as non-trivial since e.g. the same claim fails to be true for FCI algorithm, another "accelerator" of CI, developed in Spirtes:93.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/30/2017

Restricted Causal Inference Algorithm

This paper proposes a new algorithm for recovery of belief network struc...
research
05/25/2020

Non-Destructive Sample Generation From Conditional Belief Functions

This paper presents a new approach to generate samples from conditional ...
research
05/30/2018

Too Fast Causal Inference under Causal Insufficiency

Causally insufficient structures (models with latent or hidden variables...
research
01/10/2019

Conditional independence ideals with hidden variables

We study a class of determinantal ideals that are related to conditional...
research
05/07/2021

Use of High Dimensional Modeling for automatic variables selection: the best path algorithm

This paper presents a new algorithm for automatic variables selection. I...
research
04/02/2019

New relations and separations of conjectures about incompleteness in the finite domain

Our main results are in the following three sections: 1. We prove new ...

Please sign up or login with your details

Forgot password? Click here to reset