Hybrid Bayesian network discovery with latent variables by scoring multiple interventions

نویسندگان

چکیده

Abstract In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning and inference. However, Markov equivalence class considerations mean it not always possible to establish edge orientations, which why many BN structure learning algorithms cannot orientate all from purely observational data. Moreover, latent confounders can lead false positive edges. Relatively few methods have been proposed address these issues. this work, we present hybrid mFGS-BS (majority rule Fast Greedy Search with Scoring) algorithm discrete data that involves an set one or more interventional sets. The assumes insufficiency in presence variables produces a Partial Ancestral Graph (PAG). Structure relies on approach novel scoring paradigm calculates posterior probability each directed being added learnt graph. Experimental results based well-known networks up 109 10 k sample size show improves accuracy relative state-of-the-art computationally efficient.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints

Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and output only one such model. In contrast, Bayesian methods can generate and probabilistically score ...

متن کامل

Learning Linear Bayesian Networks with Latent Variables

This work considers the problem of learning linear Bayesian networks when some of the variables are unobserved. Identifiability and efficient recovery from low-order observable moments are established under a novel graphical constraint. The constraint concerns the expansion properties of the underlying directed acyclic graph (DAG) between observed and unobserved variables in the network, and it...

متن کامل

Hybrid Bayesian Networks with Linear Deterministic Variables

When a hybrid Bayesian network has conditionally deterministic variables with continuous parents, the joint density function for the continuous variables does not exist. Conditional linear Gaussian distributions can handle such cases when the continuous variables have a multi-variate normal distribution and the discrete variables do not have continuous parents. In this paper, operations require...

متن کامل

Causal discovery for linear cyclic models with latent variables

We consider the problem of identifying the causal relationships among a set of variables in the presence of both feedback loops and unmeasured confounders. This is a challenging task which, for full identification, typically requires the use of randomized experiments. For linear systems, Eberhardt et al (2010) recently provided a procedure for integrating data from several experiments, and gave...

متن کامل

Model Criticism of Bayesian Networks with Latent Variables

The application of Bayesian networks (BNs) to cognitive assessment and intelligent tutoring systems poses new challenges for model construction. When cognitive task analyses suggest constructing a BN with several latent variables, empirical model criticism of the latent structure becomes both critical and complex. This paper introduces a methodology for criticizing models both globally (a BN in...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Data Mining and Knowledge Discovery

سال: 2022

ISSN: ['1573-756X', '1384-5810']

DOI: https://doi.org/10.1007/s10618-022-00882-9