24 papers · ranked by Valyu relevance
Nicholas Tagliapietra, Katharina Ensinger, Christoph Zimmer, Osman Mian
Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics typically either discretize time —leading to poor performance on irregularly sampled data— or ignore the underlying causality. We propose…
Jingwen Jin, Peter Zeidman, Karl J. Friston, Roman Kotov
Introduction: Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This…
Kyesam Jung, Jiyoung Kang, Seungsoo Chung, Hae-Jeong Park
Multi-photon calcium imaging (CaI) is an important tool to assess activity among neural populations within a column in the sensory cortex. However, the complex asymmetrical interactions among neural populations, termed effective connectivity, cannot be directly assessed by measuring the activity of each neuron using…
Catherine Sibert, Holly Sue Hake, Andrea Stocco
Researchers agree input from the basal ganglia (BG) to the prefrontal cortex (PFC) plays an important role in cognition, but they disagree on its computational properties. Theoretical models characterize the majority of BG input as either direct (directly transmitting information to the PFC), or modulatory (indirectly…
Rebecca N. van den Honert, Sarah Shultz, Marcia K. Johnson, Gregory McCarthy
Achieving a mechanistic explanation of brain function requires understanding causal relationships among regions. A relatively new technique to assess effective connectivity in fMRI data is Dynamic Causal Modeling (DCM). As DCM is more frequently used, it becomes increasingly important to further validate the technique…
Yi Yu, Tetsunari Inamura
Current embodied world models are primarily optimized for predictive objectives, limiting their ability to generalize under distribution shifts and reason systematically about unseen situations and hypothetical interventions. We argue that embodied intelligence should move beyond predictive world modeling toward…
Cheng He, Jia Ren, Wenjian Liu, Leonardo Ricci
The Hong Kong and Macao Special Administrative Regions, situated within China’s Guangdong-Hong Kong-Macao Greater Bay Area, significantly influence and are impacted by their air quality conditions. Rapid urbanization, high population density, and air pollution from diverse factors present challenges, making the health…
James R.H. Cooke, W. Pieter Medendorp
Causal inference, the process of inferring the causes of our sensory input, is central to multisensory perception. While most computational models of causal inference focus on static perceptual tasks with no temporal or motor components, real-world behavior unfolds dynamically and often involves closed-loop control.…
Wang, Jianian, Song, Rui
To represent the causal relationships between variables, a directed acyclic graph (DAG) is widely utilized in many areas, such as social sciences, epidemics, and genetics. Many causal structure learning approaches are developed to learn the hidden causal structure utilizing deep-learning approaches. However, these…
Xuefei Cao, Björn Sandstede, Xi Luo
Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, complex differential equation modeling methods (e.g., Dynamic Causal Modeling) are…
Jia Li, Xiang Li
The traditional i.i.d.-based learning paradigm faces inherent challenges in addressing causal relationships, which has become increasingly evident with the rise of applications in causal representation learning. Our understanding of causality naturally requires a perspective as the creator rather than observer, as the…
Graham Tierney, Christoph Hellmayr, Greg Barkimer, Mike West
Bayesian dynamic modeling and forecasting is developed in the setting of sequential time series analysis for causal inference. Causal evaluation of sequentially observed time series data from control and treated units focuses on the impacts of interventions using synthetic control constructs. Methodological…
Mark Voortman, Denver Dash, Marek J. Drużdżel
In this paper, we present the Difference-Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove…
Kyuri Park, Vítor V. Vasconcelos, Mike Lees
Understanding how network structure influences system dynamics is essential for advancing psychological modeling. This tutorial introduces the causalnet R package, which enables researchers to systematically enumerate candidate directed networks by orienting a user-specified undirected or partially directed adjacency…
Ran Zhu, Yingxi Wu, Xiaoya Wang, Leran Chen + 4 more
Urban traffic congestion prediction is an important problem in smart city sensing and intelligent traffic governance. Existing methods mostly rely on single-source traffic flow sensing data or static road topology, making it difficult to sufficiently characterize the dynamic congestion propagation process driven by…
Weronika Dziarnowska, Melis Orhun, Yannan Zhu, Nils Kohn + 2 more
The interplay between emotion and memory is a central topic in cognitive neuroscience, with open questions about the underlying neuronal mechanisms. This article studies dynamic interactions among the hippocampus, amygdala, and orbitofrontal cortex during an fMRI associative memory encoding task. Participants were…
Tahereh S. Zarghami
Network representation has been a groundbreaking concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures of centrality. Notably, recent work suggests that…
Bruno Souza, Manuel Castro, Ahmed Esmin, Leonardo Machado + 2 more
Causal reasoning is essential for understanding relationships and guiding decision-making in different applications, as it allows for the identification of cause-and-effect relationships between variables. By uncovering the underlying process that drives these relationships, causal reasoning enables more accurate…
Daniel V. Holt, Magda Osman
Much of human decision making occurs in dynamic situations where decision makers have to control a number of interrelated elements (dynamic systems control). Although in recent years progress has been made toward assessing individual differences in control performance, the cognitive processes underlying exploration and…
Zhenjiang Fan, Mengrui Zhang, Summer Han
Causal relationship identification is a fundamental and complex research challenge that spans multiple disciplines, including biology, epidemiology, economics, and philosophy. Various scoring techniques and independence tests, such as local scores (e.g., Degenerate Gaussian (DG) and Bayesian Information Criterion…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…