Objection-Based Causal Networks

03/13/2013
by   Adnan Darwiche, et al.
0

This paper introduces the notion of objection-based causal networks which resemble probabilistic causal networks except that they are quantified using objections. An objection is a logical sentence and denotes a condition under which a, causal dependency does not exist. Objection-based causal networks enjoy almost all the properties that make probabilistic causal networks popular, with the added advantage that objections are, arguably more intuitive than probabilities.

READ FULL TEXT

page 1

page 3

page 6

research
02/20/2023

Causal Razors

When performing causal discovery, assumptions have to be made on how the...
research
03/06/2013

Causal Modeling

Causal Models are like Dependency Graphs and Belief Nets in that they pr...
research
03/27/2013

MCE Reasoning in Recursive Causal Networks

A probabilistic method of reasoning under uncertainty is proposed based ...
research
03/13/2013

aHUGIN: A System Creating Adaptive Causal Probabilistic Networks

The paper describes aHUGIN, a tool for creating adaptive systems. aHUGIN...
research
03/23/2021

Extracting Causal Visual Features for Limited label Classification

Neural networks trained to classify images do so by identifying features...
research
01/26/2017

The Causal Frame Problem: An Algorithmic Perspective

The Frame Problem (FP) is a puzzle in philosophy of mind and epistemolog...
research
08/28/2020

Causal blankets: Theory and algorithmic framework

We introduce a novel framework to identify perception-action loops (PALO...

Please sign up or login with your details

Forgot password? Click here to reset