NDLI: Replacing Causal Faithfulness with Algorithmic Independence of Conditionals

Content Provider	Springer Nature Link
Author	Lemeire, Jan Janzing, Dominik
Copyright Year	2012
Abstract	Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit of FF and IC is to reject causal graphs for which the joint distribution looks ‘non-generic’. The difference lies in the notion of genericity: FF sometimes rejects models just because one of the CPDs is simple, for instance if the CPD describes a deterministic relation. IC does not behave in this undesirable way. It only rejects a model when there is a non-generic relation between different CPDs although each CPD looks generic when considered separately. Moreover, it detects relations between CPDs that cannot be captured by conditional independences. IC therefore helps in distinguishing causal graphs that induce the same conditional independences (i.e., they belong to the same Markov equivalence class). The usual justification for FF implicitly assumes a prior that is a probability density on the parameter space. IC can be justified by Solomonoff’s universal prior, assigning non-zero probability to those points in parameter space that have a finite description. In this way, it favours simple CPDs, and therefore respects Occam’s razor. Since Kolmogorov complexity is uncomputable, IC is not directly applicable in practice. We argue that it is nevertheless helpful, since it has already served as inspiration and justification for novel causal inference algorithms.
Starting Page	227
Ending Page	249
Page Count	23
File Format	PDF
ISSN	09246495
Journal	Minds and Machines
Volume Number	23
Issue Number	2
e-ISSN	15728641
Language	English
Publisher	Springer Netherlands
Publisher Date	2012-07-22
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Causality Causal learning Bayesian networks Kolmogorov complexity Artificial Intelligence (incl. Robotics) Philosophy of Mind Game Theory, Economics, Social and Behav. Sciences Cognitive Psychology Philosophy of Science Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Philosophy Artificial Intelligence

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Lewisian Logic of Causal Counterfactuals

The power of intervention

Arturo Carsetti: Epistemic Complexity and Knowledge Construction

Intuitive Expertise and Perceptual Templates

Olaf Sporns: Networks of the Brain

Jakob Hohwy: The Predictive Mind

Detection of Unfaithfulness and Robust Causal Inference

Stanley B. Klein: The Two Selves—Their Metaphysical Commitments and Functional Independence

A Logical Hole the Chinese Room Avoids

Replacing Causal Faithfulness with Algorithmic Independence of Conditionals

Similar Documents

A Lewisian Logic of Causal Counterfactuals

The power of intervention

Arturo Carsetti: Epistemic Complexity and Knowledge Construction

Intuitive Expertise and Perceptual Templates

Olaf Sporns: Networks of the Brain

Jakob Hohwy: The Predictive Mind

Detection of Unfaithfulness and Robust Causal Inference

Stanley B. Klein: The Two Selves—Their Metaphysical Commitments and Functional Independence

A Logical Hole the Chinese Room Avoids

Replacing Causal Faithfulness with Algorithmic Independence of Conditionals