Structure and Parameter Learning for Causal Independence and Causal Interaction Models

by   Christopher Meek, et al.

This paper discusses causal independence models and a generalization of these models called causal interaction models. Causal interaction models are models that have independent mechanisms where a mechanism can have several causes. In addition to introducing several particular types of causal interaction models, we show how we can apply the Bayesian approach to learning causal interaction models obtaining approximate posterior distributions for the models and obtain MAP and ML estimates for the parameters. We illustrate the approach with a simulation study of learning model posteriors.



page 1

page 2

page 3

page 4


A New Look at Causal Independence

Heckerman (1993) defined causal independence in terms of a set of tempor...

Representing Independence Models with Elementary Triplets

In an independence model, the triplets that represent conditional indepe...

"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models

This paper examines the interdependence generated between two parent nod...

ABC for model selection and parameter estimation of drill-string bit-rock interaction models and stochastic stability

The bit-rock interaction considerably affects the dynamics of a drill st...

Group invariance principles for causal generative models

The postulate of independence of cause and mechanism (ICM) has recently ...

Causal Mechanism-based Model Construction

We propose a framework for building graphical causal model that is based...

A theory of independent mechanisms for extrapolation in generative models

Deep generative models reproduce complex empirical data but cannot extra...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.