A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data
[article]
2022
arXiv
pre-print
Preserved Fulltext
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
2022
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2016 pre-print arXiv:1611.03977v1 fatcat:gejrgtsagvbwhmh2mzp6gjnom4
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]
2009
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2021
Studies in History and Philosophy of Science Part A
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2014
Transactions of the Japanese society for artificial intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2017
Applied Soft Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2016
NeuroImage
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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]
2020
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
Frontiers in Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2011
Behavioral and Brain Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2011
Behavioral and Brain Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 2,019 results