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Information Theoretic Causal Effect Quantification

Aleksander Wieczorek, Volker Roth
2019 Entropy  
In this paper, we propose an information theoretic framework for causal effect quantification.  ...  To this end, we formulate a two step causal deduction procedure in the Pearl and Rubin frameworks and introduce its equivalent which uses information theoretic terms only.  ...  Causal Deduction with Information Theory We now proceed to lay out the two-step procedure for information theoretic causal effect quantification.  ... 
doi:10.3390/e21100975 fatcat:ttkyrefhdvclzeuqtpuru4o2yi

What it is like to be a bit: an integrated information decomposition account of emergent mental phenomena

Andrea I Luppi, Pedro A M Mediano, Fernando E Rosas, David J Harrison, Robin L Carhart-Harris, Daniel Bor, Emmanuel A Stamatakis
2021 Neuroscience of Consciousness  
In other words, we propose a shift from quantification of consciousness—viewed as integrated information—to its decomposition.  ...  Furthermore, we show that two organisms may attain the same amount of integrated information, yet differ in their information-theoretic composition.  ...  'Technical preliminaries') enables an effective quantification of consciousness.  ... 
doi:10.1093/nc/niab027 pmid:34804593 pmcid:PMC8600547 fatcat:woxknmmo4jflbfy4an7bo5z7o4

A fuzzy Bayesian network approach to improve the quantification of organizational influences in HRA frameworks

Peng-cheng Li, Guo-hua Chen, Li-cao Dai, Li Zhang
2012 Safety Science  
Organizational factors are the major root causes of human errors, while there have been no formal causal model of human behavior to model the effects of organizational factors on human reliability.  ...  The purpose of this paper is to develop a fuzzy Bayesian network (BN) approach to improve the quantification of organizational influences in HRA (human reliability analysis) frameworks.  ...  In order to model the causal relationships between PSFs including organizational factors to improve the quantification level of HRA, several of the methods use variations of the Bayesian Belief Network  ... 
doi:10.1016/j.ssci.2012.03.017 fatcat:tj7bqit2frfynevr5zhc4mfuki

An Information-Theoretic Quantification of Discrimination with Exempt Features

Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, Pulkit Grover
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this work, we propose a novel information-theoretic decomposition of the total discrimination (in a counterfactual sense) into a non-exempt component, which quantifies the part of the discrimination  ...  We then demonstrate that our proposed quantification of non-exempt discrimination satisfies all of them.  ...  In practice, this can inform which measure can be used when, e.g., I(Z; Ŷ |X c ) can be used when cancellation of influences (Counterexample 2) does not occur (i.e., if the SCM satisfies certain faithfulness  ... 
doi:10.1609/aaai.v34i04.5794 fatcat:emsf6f3qnrejvgk4ksaaeedfue

Non-parametric analysis of Granger causality using local measures of divergence

Mehrdad Jafari-Mamaghani
2013 Applied Mathematical Sciences  
Crucially, we i) investigate the bandwidth selection issue in kernel density estimation, and ii) discuss and propose a solution to the sensitivity of estimated information theoretic measures of divergence  ...  The employment of Granger causality analysis on temporal data is now a standard routine in many scientific disciplines.  ...  This particular definition of causality presumes a temporal signal asymmetry where the cause precedes the effect and where the information embedded in the causal variable about the occurrence of the effect  ... 
doi:10.12988/ams.2013.35275 fatcat:wbln5a27hfbexalfmkmn5jbw7u

Framework for a Bayesian Network version of IDHEAS [chapter]

K Zwirglmaier, D Straub, K Groth
2015 Safety and Reliability of Complex Engineered Systems  
(5 In this paper we propose to have an expanded BN structure qualitatively revealing the theoretical background of the method and a reduced structure, which enables a more straightforward quantification  ...  The efficiency of BNs for quantification is based on independence assumptions that ideally follow from a causal model.  ... 
doi:10.1201/b19094-416 fatcat:l6ojcybdznfurbojjspekipwsi

Page 871 of Psychological Abstracts Vol. 86, Issue 3 [page]

1999 Psychological Abstracts  
(U Chicago, Chicago, IL) Modeling causal integration and availability of information during comprehension of narrative texts.  ...  Finally, the model enabled measurement of the effects of reading and re-reading on integration of causal antecedents and consequents separately. 6887.  ... 

Explanation and quantification in educational research: the arguments of critical and scientific realism

Roy Nash
2005 British Educational Research Journal  
a very different stance on quantification.  ...  This will not be true of all poor families, but that does not mean that it is poor-quality childcare rather than the effects of poverty that is the cause of Explanation and quantification in educational  ... 
doi:10.1080/0141192052000340206 fatcat:6kjpf3s5ozc2vfx3a5lxepnumu

Program Explanation: A General Perspective

Frank Jackson, Philip Pettit
1990 Analysis  
This is reasonable, and in two ways: strategically and theoretically.  ...  A causally efficacious property with regard to an effect is a property in virtue of whose instantiation, at least in part, the effect occurs; the instance of the property helps to produce the effect and  ... 
doi:10.2307/3328853 fatcat:5qdjtrmauncrzepiyagphmi7me

Program explanation: a general perspective

F. Jackson, P. Pettit
1990 Analysis  
This is reasonable, and in two ways: strategically and theoretically.  ...  A causally efficacious property with regard to an effect is a property in virtue of whose instantiation, at least in part, the effect occurs; the instance of the property helps to produce the effect and  ... 
doi:10.1093/analys/50.2.107 fatcat:r3n7zuhyqrbd5aahjjaa3qfwgi

Is there a role for statistics in artificial intelligence? [article]

Sarah Friedrich, Gerd Antes, Sigrid Behr, Harald Binder, Werner Brannath, Florian Dumpert, Katja Ickstadt, Hans Kestler, Johannes Lederer, Heinz Leitgöb, Markus Pauly, Ansgar Steland (+2 others)
2020 arXiv   pre-print
Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development.  ...  contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality  ...  Here, it is important to differentiate theoretically informed between the different relationships covariates can have to treatment and outcome in order to avoid bias in the estimation of causal effects  ... 
arXiv:2009.09070v1 fatcat:4ckxnizrv5dnpp3r3xxat2swzy

Detection and quantification of inbreeding depression for complex traits from SNP data

Loic Yengo, Zhihong Zhu, Naomi R. Wray, Bruce S. Weir, Jian Yang, Matthew R. Robinson, Peter M. Visscher
2017 Proceedings of the National Academy of Sciences of the United States of America  
Quantifying the effects of inbreeding is critical to characterizing the genetic architecture of complex traits.  ...  We demonstrate that heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs biases ID estimates, and we develop an approach to correct this bias using LD and minor allele frequency  ...  Results Theoretical Determinants of Unbiased Estimation of ID.  ... 
doi:10.1073/pnas.1621096114 pmid:28747529 pmcid:PMC5558994 fatcat:ngz5bttc6zaydj5lehf5chiiqu

FO(C): A Knowledge Representation Language of Causality [article]

Bart Bogaerts, Joost Vennekens, Marc Denecker, Jan Van den Bussche
2014 arXiv   pre-print
By comparison, existing formalisms for representing knowledge about causal relations are quite limited in the kind of specifications of causes and effects they allow.  ...  Cause-effect relations are an important part of human knowledge. In real life, humans often reason about complex causes linked to complex effects.  ...  The rest of this paper is structured as follows: we start by introducing causal effect expressions (CEEs) and their informal semantics in Section 2.  ... 
arXiv:1405.1833v2 fatcat:rwos63wctravffversi73vilym

Nonlocal Quantum Information Transfer Without Superluminal Signalling and Communication

Jan Walleczek, Gerhard Grössing
2016 Foundations of physics  
An effective non-signalling theorem allows for nonlocal quantum information transfer yet - at the same time - effectively denies superluminal signalling and communication.  ...  In search of a decisive communication-theoretic criterion for differentiating between "axiomatic" and "effective" non-signalling, we employ the operational framework offered by Shannon's mathematical theory  ...  a sender-receiver pair-of informationally-correlated detector clicks.  ... 
doi:10.1007/s10701-016-9987-9 fatcat:3p623xujcvak7bvjs6qqcnsymy

Cardiovascular and cardiorespiratory coupling analyses: a review

S. Schulz, F.-C. Adochiei, I.-R. Edu, R. Schroeder, H. Costin, K.-J. Bar, A. Voss
2013 Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences  
[50] , thus bridging AR and information-theoretic approaches to data-driven causal inference.  ...  It can detect direct and indirect causal information transfer because it measures exclusively direct effects between signals in multivariate dynamic systems.  ... 
doi:10.1098/rsta.2012.0191 pmid:23858490 fatcat:wlcixroxarbhvejeum2kxrjjbu
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