
An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation
In a previous paper [Pearl and Verma, 1991] we presented an algorithm fo...
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Model Explanations via the Axiomatic Causal Lens
Explaining the decisions of blackbox models has been a central theme in...
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Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory
The ability to integrate information in the brain is considered to be an...
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Causal inference and constructed measures: towards a new model of measurement for psychosocial constructs
Psychosocial constructs can only be assessed indirectly, and measures ar...
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Integrated information increases with fitness in the evolution of animats
One of the hallmarks of biological organisms is their ability to integra...
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A New Measure of Conditional Dependence
Measuring conditional dependencies among the variables of a network is o...
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Supervising Feature Influence
Causal influence measures for machine learnt classifiers shed light on t...
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Complexity as Causal Information Integration
Complexity measures in the context of the Integrated Information Theory of consciousness try to quantify the strength of the causal connections between different neurons. This is done by minimizing the KLdivergence between a full system and one without causal connections. Various measures have been proposed and compared in this setting. We will discuss a class of information geometric measures that aim at assessing the intrinsic causal influences in a system. One promising candidate of these measures, denoted by Φ_CIS, is based on conditional independence statements and does satisfy all of the properties that have been postulated as desirable. Unfortunately it does not have a graphical representation which makes it less intuitive and difficult to analyze. We propose an alternative approach using a latent variable which models a common exterior influence. This leads to a measure Φ_CII, Causal Information Integration, that satisfies all of the required conditions. Our measure can be calculated using an iterative information geometric algorithm, the emalgorithm. Therefore we are able to compare its behavior to existing integrated information measures.
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