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Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
[article]
2018
arXiv
pre-print
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Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships. ...
The current state-of-the-art methods such as order MCMC are faster than previous methods but prevent the use of many natural structural priors and still have running time exponential in the maximum indegree ...
Caroline Uhler was supported in part by NSF (DMS-1651995), ONR (N00014-17-1-2147), and a Sloan Fellowship. ...
arXiv:1803.05554v3
fatcat:chf4qviitzenfpm6x66jav2mrq
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
[article]
2021
arXiv
pre-print
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Aiming to overcome this issue, we propose a form of variational inference over the graphs of Structural Causal Models (SCMs). ...
While Bayesian causal inference allows to do so, the posterior over DAGs becomes intractable even for a small number of variables. ...
A Structural Causal Model (SCM) [29] over X is defined as set of structural assignments X i := f i (x π G (i) , i ) , i = 1, ..., d (1) corresponding to a Directed Acyclic Graph (DAG) G with vertices ...
arXiv:2106.07635v1
fatcat:rjiotac6nvhlla6tnv2caoncia
Efficient Sampling and Structure Learning of Bayesian Networks
[article]
2021
arXiv
pre-print
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Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high dimensional data, and even to facilitate causal discovery. ...
Learning the underlying network structure, which is encoded as a directed acyclic graph (DAG) is highly challenging mainly due to the vast number of possible networks in combination with the acyclicity ...
Acknowledgements The authors would like to thank Markus Kalisch and Niko Beerenwinkel for useful comments and discussions. ...
arXiv:1803.07859v4
fatcat:yrqmoqyzenccpik66eab657tou
D'ya Like DAGs? A Survey on Structure Learning and Causal Discovery
2022
ACM Computing Surveys
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Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. ...
We provide a review of background theory and a survey of methods for structure discovery. ...
Firstly, the model x i = f i (pa i , u i ), i = 1..., n, is a Structural equation/Causal Model (SEM/SCM) which indicates assignment of the value x i in the space of X to a function of its structural parents ...
doi:10.1145/3527154
fatcat:sroohzvx5reajkia5ythaiyyjm
Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
[article]
2019
arXiv
pre-print
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To illustrate the approach, we consider a variety of causal structure learning tasks for continuous and discrete data. ...
In this work, we consider models with latent permutations and propose control variates for the Plackett-Luce distribution. ...
The authors thank NRU HSE for providing computational resources, NVIDIA for GPU donations, and Amazon for AWS Cloud Credits. ...
arXiv:1911.10036v1
fatcat:f7ghfmu2afcq5g7ieb7f5weho4
Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: Beyond edge weights in psychological networks
2017
2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
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Monte Carlo methods for full Bayesian model averaging over model structures require considerable computational resources, whereas bootstrapped graphical lasso and its approximations offer scalable alternatives ...
Uncertainty over model structures poses a challenge for many approaches exploring effect strength parameters at system-level. ...
The sponsors funded the work, but had no further role in the design of the study, in data collection or analysis, in the decision to publish, or in the preparation, review, or approval of the manuscript ...
doi:10.1109/cibcb.2017.8058566
dblp:conf/cibcb/HullamJDA17
fatcat:a67mkpbvwbeyzhot3iwxj6tjwi
A Bayesian View of Challenges in Feature Selection: Feature Aggregation, Multiple Targets, Redundancy and Interaction
2008
Journal of machine learning research
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Now we formulate refined levels in BMLA by introducing the concepts of k-MBSs and k-MBGs, which are intermediate, scalable model properties expressing relevance. ...
We introduce and investigate a score for feature redundancy and interaction based on the decomposability of the structure posterior. ...
In ( 16 ), Madigan et al. proposed a Markov Chain Monte Carlo (MCMC) scheme to approximate such Bayesian inference. The MCMC method over the DAG space was improved by Castelo et al. (12) . ...
dblp:journals/jmlr/AntalMHSF08
fatcat:dhy62s5a3vbmpewohgayblcz74
pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data
[article]
2018
arXiv
pre-print
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Then, Bayesian learning carefully encodes the local and ST causal relations with a Gaussian Bayesian network (GBN)-based graphical model, which also integrates environmental influences to minimize biases ...
In this paper, we use urban big data (air quality data and meteorological data) to identify the spatiotemporal (ST) causal pathways for air pollutants. ...
Pearl's causality model [5] encodes the causal relationships in a directed acyclic graph (DAG) [23] for probabilistic inference. ...
arXiv:1610.07045v3
fatcat:vs22xf233rfqbaddofx6muw2ze
A survey of Bayesian Network structure learning
[article]
2021
arXiv
pre-print
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However, determining the graphical structure of a BN remains a major challenge, especially when modelling a problem under causal assumptions. ...
Approaches for dealing with data noise in real-world datasets and incorporating expert knowledge into the learning process are also covered. ...
Pearl (1998) showed that this creates a minimal I-MAP DAG for that node ordering. ...
arXiv:2109.11415v1
fatcat:tj4ceig7rvbb3k4bqffvdvvwye
pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data
2017
IEEE Transactions on Big Data
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Then, Bayesian learning carefully encodes the local and ST causal relations with a Gaussian Bayesian Network (GBN)-based graphical model, which also integrates environmental influences to minimize biases ...
In this paper, we use urban big data (air quality data and meteorological data) to identify the spatiotemporal (ST) causal pathways for air pollutants. ...
Pearl's causality model [5] encodes the causal relationships in a directed acyclic graph (DAG) [23] for probabilistic inference. ...
doi:10.1109/tbdata.2017.2723899
fatcat:kf4hq6yygrdizj6ift5m3lfhz4
A Bayesian Active Learning Experimental Design for Inferring Signaling Networks
2018
Journal of Computational Biology
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To learn the causal patterns of influence between proteins in a regulatory network, the methods require experiments that include targeted interventions. ...
Our approach takes as input pathway databases such as KEGG and historic datasets in repositories such as Cytobank, expresses them in form of prior probability distributions on the signaling network structures ...
Scutari for guidance in using the R package bnlearn. This work was supported in part by the NSF CAREER award DBI-1054826, and by the Sy and Laurie Sternberg award to OV. ...
doi:10.1089/cmb.2017.0247
pmid:29927613
fatcat:humwqmytfbexze3weng2pejvxi
Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks
[article]
2018
arXiv
pre-print
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We go one step further and advocate for the consideration of causal representations of the interactions in living systems.We present the causal formalism and bring it out in the context of biological networks ...
We also discuss its ability to decipher the causal information flow as observed in gene expression. ...
Lastly, we are very grateful to reviewers of this chapter for their insightful comments. ...
arXiv:1805.01608v1
fatcat:gcvsm6pkgvhmhnswo5vb22fkbe
High-throughput Bayesian network learning using heterogeneous multicore computers
2010
Proceedings of the 24th ACM International Conference on Supercomputing - ICS '10
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The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. ...
However, learning the structure of Bayesian Networks (BNs) is an NPhard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20-50 nodes). ...
and David Dill for their help with precision analysis. ...
doi:10.1145/1810085.1810101
pmid:28819655
pmcid:PMC5557010
dblp:conf/ics/LindermanBAMAN10
fatcat:jtrq4z3fdzdtbfh6svqu7yepkq
DAGs with No Curl: An Efficient DAG Structure Learning Approach
[article]
2021
arXiv
pre-print
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in this equivalent set of DAGs. ...
To further improve efficiency, we propose a novel learning framework to model and learn the weighted adjacency matrices in the DAG space directly. ...
Changhe Yuan for helpful discussions. We thank to anonymous reviewers who have provide helpful comments. Y. ...
arXiv:2106.07197v1
fatcat:4bxn3ofwlfe4bj7icfk2wj4uqe
A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies
2011
BMC Bioinformatics
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In order to tackle the learning of both forest structure and probability distributions, a generic algorithm has been proposed. ...
Conclusions: The forest of hierarchical latent class models offers several advantages for genome-wide association studies: accurate modeling of linkage disequilibrium, flexible data dimensionality reduction ...
Acknowledgements The authors are grateful to two anonymous referees for constructive comments and help in improving their manuscript. ...
doi:10.1186/1471-2105-12-16
pmid:21226914
pmcid:PMC3033325
fatcat:dbgrxmj46zcn5cr52e2x47hrsa
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