Learning domain-specific causal discovery from time series
- URL: http://arxiv.org/abs/2209.05598v3
- Date: Tue, 10 Oct 2023 00:45:46 GMT
- Title: Learning domain-specific causal discovery from time series
- Authors: Xinyue Wang, Konrad Paul Kording
- Abstract summary: Causal discovery from time-varying data is important in neuroscience, medicine, and machine learning.
Human expertise is often not entirely accurate and tends to be outperformed in domains with abundant data.
In this study, we examine whether we can enhance domain-specific causal discovery for time series using a data-driven approach.
- Score: 7.298647409503783
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Causal discovery (CD) from time-varying data is important in neuroscience,
medicine, and machine learning. Techniques for CD encompass randomized
experiments, which are generally unbiased but expensive, and algorithms such as
Granger causality, conditional-independence-based, structural-equation-based,
and score-based methods that are only accurate under strong assumptions made by
human designers. However, as demonstrated in other areas of machine learning,
human expertise is often not entirely accurate and tends to be outperformed in
domains with abundant data. In this study, we examine whether we can enhance
domain-specific causal discovery for time series using a data-driven approach.
Our findings indicate that this procedure significantly outperforms
human-designed, domain-agnostic causal discovery methods, such as Mutual
Information, VAR-LiNGAM, and Granger Causality on the MOS 6502 microprocessor,
the NetSim fMRI dataset, and the Dream3 gene dataset. We argue that, when
feasible, the causality field should consider a supervised approach in which
domain-specific CD procedures are learned from extensive datasets with known
causal relationships, rather than being designed by human specialists. Our
findings promise a new approach toward improving CD in neural and medical data
and for the broader machine learning community.
Related papers
- Exploring Causal Learning through Graph Neural Networks: An In-depth
Review [12.936700685252145]
We introduce a novel taxonomy that encompasses various state-of-the-art GNN methods employed in studying causality.
GNNs are further categorized based on their applications in the causality domain.
This review also touches upon the application of causal learning across diverse sectors.
arXiv Detail & Related papers (2023-11-25T10:46:06Z) - Source-Free Collaborative Domain Adaptation via Multi-Perspective
Feature Enrichment for Functional MRI Analysis [55.03872260158717]
Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis.
Many methods have been proposed to reduce fMRI heterogeneity between source and target domains.
But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies.
We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
arXiv Detail & Related papers (2023-08-24T01:30:18Z) - Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks.
We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
arXiv Detail & Related papers (2023-05-25T16:29:16Z) - A Survey on Causal Discovery Methods for I.I.D. and Time Series Data [4.57769506869942]
Causal Discovery (CD) algorithms can identify the cause-effect relationships among the variables of a system from related observational data.
We present an extensive discussion on the methods designed to perform causal discovery from both independent and identically distributed (I.I.D.) data and time series data.
arXiv Detail & Related papers (2023-03-27T09:21:41Z) - Amortized Inference for Causal Structure Learning [72.84105256353801]
Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test.
We train a variational inference model to predict the causal structure from observational/interventional data.
Our models exhibit robust generalization capabilities under substantial distribution shift.
arXiv Detail & Related papers (2022-05-25T17:37:08Z) - Learning Neural Causal Models with Active Interventions [83.44636110899742]
We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process.
Our method significantly reduces the required number of interactions compared with random intervention targeting.
We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
arXiv Detail & Related papers (2021-09-06T13:10:37Z) - The Causal Neural Connection: Expressiveness, Learnability, and
Inference [125.57815987218756]
An object called structural causal model (SCM) represents a collection of mechanisms and sources of random variation of the system under investigation.
In this paper, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020) still holds for neural models.
We introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences.
arXiv Detail & Related papers (2021-07-02T01:55:18Z) - Uncovering the structure of clinical EEG signals with self-supervised
learning [64.4754948595556]
Supervised learning paradigms are often limited by the amount of labeled data that is available.
This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG)
By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
arXiv Detail & Related papers (2020-07-31T14:34:47Z) - Incorporating structured assumptions with probabilistic graphical models
in fMRI data analysis [5.23143327587266]
We review a few recently developed algorithms in various domains of fMRI research.
These algorithms all tackle the challenges in fMRI similarly.
We advocate wider adoption of explicit model construction in cognitive neuroscience.
arXiv Detail & Related papers (2020-05-11T06:32:54Z) - Causal Discovery from Incomplete Data: A Deep Learning Approach [21.289342482087267]
Imputated Causal Learning is proposed to perform iterative missing data imputation and causal structure discovery.
We show that ICL can outperform state-of-the-art methods under different missing data mechanisms.
arXiv Detail & Related papers (2020-01-15T14:28:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.