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Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data [article]

Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji
2022 arXiv   pre-print
In this work, we propose Bayesian-augmented frequentist independence tests to improve the performance of constraint-based causal discovery methods under insufficient data: 1) We firstly introduce a Bayesian  ...  We apply proposed independence tests to constraint-based causal discovery methods and evaluate the performance on benchmark datasets with insufficient samples.  ...  As Conclusion In this paper, we introduce Bayesian methods for robust constraint-based causal discovery under insufficient data.  ... 
arXiv:2206.08448v1 fatcat:tjak3z575ff5nh5osr2du7n2oq

Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data

Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji
2022 Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence   unpublished
In this work, we propose Bayesian-augmented frequentist independence tests to improve the performance of constraint-based causal discovery methods under insufficient data: 1) We firstly introduce a Bayesian  ...  We apply proposed independence tests to constraint-based causal discovery methods and evaluate the performance on benchmark datasets with insufficient samples.  ...  Conclusion In this paper, we introduce Bayesian methods for robust constraint-based causal discovery under insufficient data.  ... 
doi:10.24963/ijcai.2022/668 fatcat:rnysude3ujcm7jdzhckoqgkcpm

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures [article]

Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer
2021 arXiv   pre-print
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making.  ...  While Bayesian causal inference allows to do so, the posterior over DAGs becomes intractable even for a small number of variables.  ...  PC [34] algorithm uses a constraint based approach for causal discovery.  ... 
arXiv:2106.07635v1 fatcat:rjiotac6nvhlla6tnv2caoncia

A Review on Algorithms for Constraint-based Causal Discovery [article]

Kui Yu, Jiuyong Li, Lin Liu
2016 arXiv   pre-print
As a conclusion, some open problems in constraint-based causal discovery are outlined for future research.  ...  In this paper, we aim to review the constraint-based causal discovery algorithms. Firstly, we discuss the learning paradigm of the constraint-based approaches.  ...  Finally, we briefly mention publicly-available software and data sets for constraint-based causal discovery.  ... 
arXiv:1611.03977v2 fatcat:ercpfkqssnabfgdc3ndd7bd3tu

Minimum Free Energy Principle for Constraint-Based Learning Bayesian Networks [chapter]

Takashi Isozaki, Maomi Ueno
2009 Lecture Notes in Computer Science  
Constraint-based search methods, which are a major approach to learning Bayesian networks, are expected to be effective in causal discovery tasks.  ...  However, such methods often suffer from impracticality of classical hypothesis testing for conditional independence when the sample size is insufficiently large.  ...  ., Ltd. for support and encouragement, and thanks T. Ogawa of the University of Electro-Communications for advice related to information geometry.  ... 
doi:10.1007/978-3-642-04180-8_57 fatcat:w2ixxtwnujgtlawpwwxow5ozqi

D'ya like DAGs? A Survey on Structure Learning and Causal Discovery [article]

Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
2021 arXiv   pre-print
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.  ...  Constraint-Based and Score-Based Approaches Most constraint-based approaches test for conditional independencies in the empirical joint distribution in order to construct a graph that reflects these conditional  ... 
arXiv:2103.02582v2 fatcat:x45blijl5ze5xjyuqh6vlc26oq

Scientific realism and empirical confirmation: A puzzle

Simon Allzén
2021 Studies in History and Philosophy of Science Part A  
Since IBE can be applied in scientific contexts in which empirical confirmation has not yet been reached, realists will in these contexts be committed to the existence of empirically unconfirmed objects  ...  Scientific realism driven by inference to the best explanation (IBE) takes empirically confirmed objects to exist, independent, pace empiricism, of whether those objects are observable or not.  ...  This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  ... 
doi:10.1016/j.shpsa.2021.10.008 pmid:34689059 fatcat:kvzhp7yugbc2tbykbacavpdbdq

A Robust Causal Discovery Algorithm against Faithfulness Violation

Takashi Isozaki
2014 Transactions of the Japanese society for artificial intelligence  
Methods of statistical causal discovery that use conditional independence (CI) tests are attractive due to their time efficiency and applications to latent variable systems.  ...  We propose a causal discovery algorithm that can reduce the numbers of unnecessarily performed CI tests in this study and so provide accurate and fast inference without loss of theoretical correctness.  ...  Acknowledgments The author would like to thank Manabu Kuroki for the helpful discussions and his suggestions, Ryosuke Ohata for his assistance with the experiments, and Mimpei Morishita for his comments  ... 
doi:10.1527/tjsai.29.137 fatcat:u45k5mbk7bb6tipnqqljerjlpy

Causality on cross-sectional data: Stable specification search in constrained structural equation modeling

Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes
2017 Applied Soft Computing  
Generally discovery algorithms can be divided into two approaches: constraint-based and score-based.  ...  The present work introduces a new hypothesis-free score-based causal discovery algorithm, called stable specification search, that is robust for finite samples based on recent advances in stability selection  ...  ACKNOWLEDGMENTS The research leading to these results has received funding from the DGHE of Indonesia and the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n •  ... 
doi:10.1016/j.asoc.2016.10.003 fatcat:ymkinn4se5crpphx67i3a7gdxy

Confronting Machine Learning With Financial Research [article]

Kristof Lommers, Ouns El Harzli, Jack Kim
2021 arXiv   pre-print
Moreover, we discuss the various applications of machine learning in the research process such as estimation, empirical discovery, testing, causal inference and prediction.  ...  Machine learning algorithms have been developed for certain data environments which substantially differ from the one we encounter in finance.  ...  More specifically, it can be used for various parts of the research process such as data pre-processing, estimation, empirical discovery, testing, causal inference and prediction.  ... 
arXiv:2103.00366v2 fatcat:bp4j34cenjf4lii5rgav5o5d6e

Bayesian model reduction and empirical Bayes for group (DCM) studies

Karl J. Friston, Vladimir Litvak, Ashwini Oswal, Adeel Razi, Klaas E. Stephan, Bernadette C.M. van Wijk, Gabriel Ziegler, Peter Zeidman
2016 NeuroImage  
This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level -e.g., dynamic causal models -and linear models at  ...  We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical  ...  BCMvW acknowledges support by the National Institute for Health Research UCL Hospitals Biomedical Research Centre.  ... 
doi:10.1016/j.neuroimage.2015.11.015 pmid:26569570 pmcid:PMC4767224 fatcat:mp3fllxal5cu3ea44erqm4wwsu

Probabilistic Colocalization of Genetic Variants from Complex and Molecular Traits: Promise and Limitations [article]

Abhay Hukku, Milton Pividori, Francesca Luca, Roger Pique-Regi, Hae Kyung Im, Xiaoquan Wen
2020 bioRxiv   pre-print
Colocalization analysis has emerged as a powerful tool to uncover the overlapping of causal variants responsible for both molecular and complex disease phenotypes.  ...  Consequently, we recommend the following strategies for the best practice of colocalization analysis: i) estimating prior enrichment level from the observed data; and ii) separating fine-mapping and colocalization  ...  This is because, at the SNP-level, it remains difficult to pinpoint the causal variants for both traits due to the combination of LD and insufficient sample size.  ... 
doi:10.1101/2020.07.01.182097 fatcat:ya2t3feiwve2hmzancv5mp7e7q

Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using Bayesian Networks

Bradley Butcher, Vincent S. Huang, Christopher Robinson, Jeremy Reffin, Sema K. Sgaier, Grace Charles, Novi Quadrianto
2021 Frontiers in Artificial Intelligence  
Causal Bayesian Networks (BN) have been proposed as a powerful method for discovering and representing the causal relationships from observational data as a Directed Acyclic Graph (DAG).  ...  To generate results for such a Causal Datasheet, a tool was developed which can generate synthetic Bayesian networks and their associated synthetic datasets to mimic real-world datasets.  ...  Causal inference and discovery approaches such as causal Bayesian Network (BN) can fill this void.  ... 
doi:10.3389/frai.2021.612551 fatcat:3vevt756dreqthld4cw4i6umpi

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

Matt Jones, Bradley C. Love
2011 Behavioral and Brain Sciences  
not grounded in empirical measurement.  ...  We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out.  ...  First, Bayesian inference has proven to be exceedingly valuable as an analysis tool for deciding among scientific hypotheses or models based on empirical data.  ... 
doi:10.1017/s0140525x10003134 pmid:21864419 fatcat:vdrc6bzpyzfohbzk3j7eagfw5m

The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science

Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford, Joshua B. Tenenbaum
2011 Behavioral and Brain Sciences  
not grounded in empirical measurement.  ...  We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out.  ...  First, Bayesian inference has proven to be exceedingly valuable as an analysis tool for deciding among scientific hypotheses or models based on empirical data.  ... 
doi:10.1017/s0140525x11000239 fatcat:spa6vwghifdfjonvjffbebdv5i
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