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Structure and Parameter Learning for Causal Independence and Causal Interaction Models [article]

Christopher Meek, David Heckerman
2015 arXiv   pre-print
for the models and obtain MAP and ML estimates for the parameters.  ...  This paper discusses causal independence models and a generalization of these models called causal interaction models.  ...  Acknowledgments We thank Bo Thiesson, Jack Breese, and Max Chickering for helpful discussion on this material.  ... 
arXiv:1302.1561v2 fatcat:wnwojjr7mzbfxdft6aqrs6l2zq

Learning Bayesian Networks with Restricted Causal Interactions [article]

Julian R. Neil, Chris S. Wallace, Kevin B. Korb
2013 arXiv   pre-print
We describe an alternative interpretation, and use a Minimum Message Length (MML) (Wallace, 1987) metric for structure learning of networks exhibiting causal independence, which we term first-order networks  ...  While log-linear models in this context are not new (Whittaker, 1990; Buntine, 1991; Neal, 1992; Heckerman and Meek, 1997), for structure learning they are generally subsumed under a naive Bayes model.  ...  We also tracked a discrepancy in the posterior estimates of the FON and dual network to a difference in parameter prior and estimation, which we intend to address in further work.  ... 
arXiv:1301.6727v1 fatcat:bel2u3fiqjgzznyykx3nsrgntm

EM Algorithm for Symmetric Causal Independence Models [chapter]

Rasa Jurgelenaite, Tom Heskes
2006 Lecture Notes in Computer Science  
In this paper, we present the EM algorithm to learn the parameters in causal independence models based on the symmetric Boolean function.  ...  Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks.  ...  The authors are grateful to Henk Boot and Babs Taal for the provided non-Hodgkin's lymphoma data. We would also like to thank Jiří Vomlel for sharing his code and insights.  ... 
doi:10.1007/11871842_25 fatcat:a2stdgqipbfyjj2fq3ph4rggey

Learning symmetric causal independence models

Rasa Jurgelenaite, Tom Heskes
2008 Machine Learning  
In this paper, we study the problem of learning the parameters in these models, further referred to as symmetric causal independence models.  ...  Causal independence modelling is a well-known method for reducing the size of probability tables, simplifying the probabilistic inference and explaining the underlying mechanisms in Bayesian networks.  ...  Acknowledgements The authors are grateful to Henk Boot and Babs Taal for providing the non-Hodgkin's lymphoma data, Gytis Karčiauskas for the preprocessed Reuters data and Jiří Vomlel for sharing his code  ... 
doi:10.1007/s10994-007-5041-7 fatcat:boyegxqbmje6dfxecrkur24jla

Using Bayesian networks to discover relations between genes, environment, and disease

Chengwei Su, Angeline Andrew, Margaret R Karagas, Mark E Borsuk
2013 BioData Mining  
Allowing for network structures that depart from a strict causal interpretation also enhances our ability to discover complex associations including gene-gene (epistasis) and gene-environment interactions  ...  We then describe a variety of algorithms for learning the structure of a network from observational data.  ...  Acknowledgements Research supported by grants from the National Center for Research Resources (5P20RR024474-02) and the National Institute of General Medical Sciences (8 P20 GM103534-02) from the National  ... 
doi:10.1186/1756-0381-6-6 pmid:23514120 pmcid:PMC3614442 fatcat:olxihsfj2jeu7ciwt55rk7xzfm

Seeing Versus Doing: Two Modes of Accessing Causal Knowledge

Michael R. Waldmann, York Hagmayer
2005 Journal of Experimental Psychology. Learning, Memory and Cognition  
The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters.  ...  We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational  ...  In this sense, causal-model theory is primarily a parameter estimation learning theory.  ... 
doi:10.1037/0278-7393.31.2.216 pmid:15755240 fatcat:h7kjpnwqofb45ktotn4f4yzkde

Playing against Nature: causal discovery for decision making under uncertainty [article]

M. Gonzalez-Soto, L.E. Sucar, H.J. Escalante
2018 arXiv   pre-print
We show that the model achieves a performance similar to the classic Q-learning while it also acquires a causal model of the environment.  ...  As proof of concept, we present an implementation of this causal decision making model and apply it in a simple scenario.  ...  We are grateful to the National Institute of Astrophysics Optics and Electronics (INAOE) and to Mrs. Graciela Soto for their generous funding in order to attend ICML 2018.  ... 
arXiv:1807.01268v1 fatcat:wiheqa5o7jcypdwif3dr4zc4tm

Observing and Intervening: Rational and Heuristic Models of Causal Decision Making

Bjorn Meder, Tobias Gerstenberg, York Hagmayer, Michael R. Waldmann
2010 Open Psychology Journal  
A Bayesian approach of rational causal inference, which aims to infer the structure and its parameters from the available data, provides the benchmark model.  ...  Recently, a number of rational theories have been put forward which provide a coherent formal framework for modeling different types of causal inferences, such as prediction, diagnosis, and action planning  ...  Within the causal Bayes nets framework, a variety of learning algorithms have been developed to infer causal structures and parameter values [12] .  ... 
doi:10.2174/1874350101003010119 fatcat:2almtly24vbb5dy3dsvrw7n4im

Noisy-OR Models with Latent Confounding [article]

Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
2012 arXiv   pre-print
We further show that identification is preserved under an extension of the model that allows for negative influences, and present learning algorithms that we test for accuracy, scalability and robustness  ...  For linear causal models with latent variables Hyttinen et al. (2010) gave precise conditions for when such data are sufficient to identify the full model.  ...  Acknowledgements We thank three anonymous reviewers for helpful comments.  ... 
arXiv:1202.3735v1 fatcat:crzu5qjf7rafvp72gu5u3swpri

Causal Discovery in Physical Systems from Videos [article]

Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox, Animesh Garg
2020 arXiv   pre-print
The causal structure assumed by the model also allows it to make counterfactual predictions and extrapolate to systems of unseen interaction graphs or graphs of various sizes.  ...  In particular, our goal is to discover the structural dependencies among environmental and object variables: inferring the type and strength of interactions that have a causal effect on the behavior of  ...  This entails jointly performing model class estimation, parameter inference and thereby building a predictive model for new latent structures at test time in a meta-learning framework.  ... 
arXiv:2007.00631v3 fatcat:n3b34ebalrhyfazf7fwwug4eyy

CausalMGM: an interactive web-based causal discovery tool

Xiaoyu Ge, Vineet K Raghu, Panos K Chrysanthis, Panayiotis V Benos
2020 Nucleic Acids Research  
visualization of the learned causal (directed) graph.  ...  Web-based CausalMGM consists of three data analysis tools: (i) feature selection and clustering; (ii) automated identification of cause-and-effect relationships via a graphical model; and (iii) interactive  ...  ACKNOWLEDGEMENTS We would like to thank Daniel Petrov for his help and insightful comments in the development of CausalMGM web server.  ... 
doi:10.1093/nar/gkaa350 pmid:32392295 pmcid:PMC7319538 fatcat:wyvody2albfptioh3ughcw7ubu

A Primer on Learning in Bayesian Networks for Computational Biology

Chris J. Needham, James R. Bradford, Andrew J. Bulpitt, David R. Westhead
2007 PLoS Computational Biology  
The authors would like to thank the Biotechnology and Biological Sciences Research Council for funding on grant BBSB16585 during which this article was written.  ...  JRB and DRW have advised on the biological examples. AJB and DRW have contributed their pedagogical knowledge on the topic. All authors have advised on the selection and presentation of the material.  ...  The learning of model structures, and particularly causal models, is difficult, and often requires careful experimental design, but can lead to the learning of unknown relationships and excellent predictive  ... 
doi:10.1371/journal.pcbi.0030129 pmid:17784779 pmcid:PMC1963499 fatcat:7dwi3yttxvdxrivompxfsi75g4

Causal Learning From Predictive Modeling for Observational Data

Nandini Ramanan, Sriraam Natarajan
2020 Frontiers in Big Data  
We consider the problem of learning structured causal models from observational data. In this work, we use causal Bayesian networks to represent causal relationships among model variables.  ...  We validate the learned models on benchmark networks and demonstrate the effectiveness when compared to some of the state-of-the-art Causal Bayesian Network Learning algorithms from observational Data.  ...  ACKNOWLEDGMENTS The authors acknowledge the support of members of STARLING lab for the discussions. We thank the reviewers for their insightful comments and in significantly improving the paper.  ... 
doi:10.3389/fdata.2020.535976 pmid:33693412 pmcid:PMC7931928 fatcat:gczn6fcbbjbzjgo3x3tcgqs75e

Application of Bayesian networks on large-scale biological data

Yi Liu, Jing-Dong J. Han
2010 Frontiers in Biology  
In this paper, we not only introduce the Bayesian network formalism and its applications in systems biology, but also review recent technical developments for scaling up or speeding up the structural learning  ...  In particular, Bayesian network (BN) is a powerful tool for the ab-initial identification of causal and non-causal relationships between biological factors directly from experimental data.  ...  Deriving causal knowledge from BN structures There are two types of interactions in systems biology, non-causal and causal.  ... 
doi:10.1007/s11515-010-0023-8 fatcat:iiepdm5eknc7njtlb2aarj7yj4

Structure and strength in causal induction☆

Thomas L. Griffiths, Joshua B. Tenenbaum
2005 Cognitive Psychology  
We present a framework for the rational analysis of elemental causal induction-learning about the existence of a relationship between a single cause and effect-based upon causal graphical models.  ...  We show that two leading rational models of elemental causal induction, DP and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure  ...  Structure learning and parameter estimation Constructing a causal graphical model from a set of observed data involves two kinds of learning: structure learning and parameter estimation.  ... 
doi:10.1016/j.cogpsych.2005.05.004 pmid:16168981 fatcat:kme3kazymrfpzcol5olyz27h2e
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