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1. Quantifying Evidence for—and against—Granger Causality with Bayes Factors

   https://doi.org/10.1080/00273171.2023.2214890  

   Oravecz, Zita; Vandekerckhove, Joachim (November 2024, Multivariate Behavioral Research)

   Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null -- we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study. 

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2. Supervised Learning via Ensemble Tensor Completion

   https://doi.org/10.1109/IEEECONF51394.2020.9443399  

   Kargas, Nikos; Sidiropoulos, Nicholas D. (November 2020, 2020 Asilomar Conference on on Signals, Systems, and Computers)

   null (Ed.)

   Learning nonlinear functions from input-output data pairs is one of the most fundamental problems in machine learning. Recent work has formulated the problem of learning a general nonlinear multivariate function of discrete inputs, as a tensor completion problem with smooth latent factors. We build upon this idea and utilize two ensemble learning techniques to enhance its prediction accuracy. Ensemble methods can be divided into two main groups, parallel and sequential. Bagging also known as bootstrap aggregation is a parallel ensemble method where multiple base models are trained in parallel on different subsets of the data that have been chosen randomly with replacement from the original training data. The output of these models is usually combined and a single prediction is computed using averaging. One of the most popular bagging techniques is random forests. Boosting is a sequential ensemble method where a sequence of base models are fit sequentially to modified versions of the data. Popular boosting algorithms include AdaBoost and Gradient Boosting. We develop two approaches based on these ensemble learning techniques for learning multivariate functions using the Canonical Polyadic Decomposition. We showcase the effectiveness of the proposed ensemble models on several regression tasks and report significant improvements compared to the single model. 

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3. Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders

   https://doi.org/10.1093/bib/bbaf089  

   Chen, Liang; Dautle, Madison; Gao, Ruoying; Zhang, Shaoqiang; Chen, Yong (March 2025, Briefings in Bioinformatics)

   Abstract The development of single-cell RNA sequencing (scRNA-seq) technology provides valuable data resources for inferring gene regulatory networks (GRNs), enabling deeper insights into cellular mechanisms and diseases. While many methods exist for inferring GRNs from static scRNA-seq data, current approaches face challenges in accurately handling time-series scRNA-seq data due to high noise levels and data sparsity. The temporal dimension introduces additional complexity by requiring models to capture dynamic changes, increasing sensitivity to noise, and exacerbating data sparsity across time points. In this study, we introduce GRANGER, an unsupervised deep learning-based method that integrates multiple advanced techniques, including a recurrent variational autoencoder, GRANGER causality, sparsity-inducing penalties, and negative binomial (NB)-based loss functions, to infer GRNs. GRANGER was evaluated using multiple popular benchmarking datasets, where it demonstrated superior performance compared to eight well-known GRN inference methods. The integration of a NB-based loss function and sparsity-inducing penalties in GRANGER significantly enhanced its capacity to address dropout noise and sparsity in scRNA-seq data. Additionally, GRANGER exhibited robustness against high levels of dropout noise. We applied GRANGER to scRNA-seq data from the whole mouse brain obtained through the BRAIN Initiative project and identified GRNs for five transcription regulators: E2f7, Gbx1, Sox10, Prox1, and Onecut2, which play crucial roles in diverse brain cell types. The inferred GRNs not only recalled many known regulatory relationships but also revealed sets of novel regulatory interactions with functional potential. These findings demonstrate that GRANGER is a highly effective tool for real-world applications in discovering novel gene regulatory relationships. 

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4. Data-driven causal analysis of observational biological time series

   https://doi.org/10.7554/eLife.72518  

   Yuan, Alex Eric; Shou, Wenying (August 2022, eLife)

   Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called ‘model-free’ causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AlV0ttQrjK8 ). 

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5. Malware Detection Using Machine Learning: A Comparative Analysis

   Mohammed, Sameeruddin; Zhang, Fan; Brishti, Faria; Chen, Baiyun Chen; Wu, Fan (July 2025, IARIA Board and IARIA Press)

   To address the growing challenges posed by Cyber threats, anti-malware organizations have increasingly turned to Machine Learning (ML). In recent years, machine learning algorithms have become indispensable for solving complex classification problems, outperforming traditional statistical methods by capturing intricate patterns in high dimensional data. However, selecting the optimal model requires rigorous evaluation in multiple performance metrics while ensuring stability across different data splits. In this study, we conducted a comprehensive assessment of eight machine learning algorithms. Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes, Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), and k-Nearest Neighbors (KNN) using stratified 5-fold cross-validation. Our results reveal that RF, LightGBM, DT, and KNN achieve exceptional performance, with identical near-perfect scores in accuracy (0.9918), precision (0.9920), recall (0.9918), F1 score (0.9918) and Area Under the Receiver Operation Characteristic Curve (AUC-ROC) (0.9998), along with remarkably low variance (10−6 to 10−8), demonstrating unparalleled robustness. The study highlights the superiority of tree-based ensembles and KNN in achieving high predictive power and stability, whereas classical algorithms such as logistic regression and naive Bayes lag. Despite XGBoost’s reputation, its performance here is eclipsed by simpler tree-based methods. Our analysis underscores the importance of considering variance when evaluating model selection, particularly for critical applications where stability is paramount, and provides actionable insights for practitioners seeking reliable, high-accuracy classifiers. 

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