GC-KAN uses KANs to detect Granger causality in time series data.
problem Detecting causal relationships in nonlinear time series data.
method Developed GC-KAN framework using Kolmogorov-Arnold networks for Granger causality detection.
result KANs outperform MLPs in identifying sparse Granger causal relationships.
The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two variables from observational data have been proposed. However, most of these deal only with purely directed causal relationships and cannot det…
Symplectic quandles can detect causality in spacetimes, improving on existing methods.
problem Detecting causality in (2+1)-dimensional spacetimes using existing methods.
method Combining Alexander-Conway polynomial with symplectic quandles.
result Symplectic quandles can distinguish between different types of links, suggesting their ability to detect causality.
CausAdv detects adversarial examples using causal reasoning.
problem Vulnerability of CNNs to adversarial perturbations.
method Causal framework based on counterfactual reasoning.
result Adversarial examples exhibit different CI distributions compared to clean samples.
CaT-GNN improves credit card fraud detection by integrating causal reasoning into GNNs.
problem Credit card fraud detection overlooks causal structure of transactions.
method CaT-GNN combines causal invariant learning and temporal graph neural networks.
result CaT-GNN outperforms existing methods on various datasets.
Empirical evidence suggests link polynomials can detect causality in spacetimes.
problem Detect causality in (2+1)-dimensional globally hyperbolic spacetimes. method Introduced a new invariant of certain tangles related to the Conway polynomial.
result The Conway polynomial does not detect causality in certain spacetime scenarios.
Framework detects changes in causal dependence between variables.
problem Detecting changes in causal dependence between variables in the presence of confounders.
method Non-parametric approach using kernel mean embeddings and copulas.
result Proposed statistic accurately detects changes in causal dependence.
Quandle coloring detects causality in spacetime links.
problem Detecting causality in spacetime links using quandle colorings.
method Conjugation quandle of dihedral group D5 combined with Alexander-Conway polynomial.
result The conjugation quandle over D5 distinguishes causally related and unrelated events.
Proposes a new model for online anomaly detection in multivariate time series.
problem Inaccurate anomaly detection in multivariate time series due to spurious correlations and lack of temporal causality.
method Clusters channels based on correlations, embeds each cluster, and integrates information through a causal mixer while maintaining temporal causality.
result Consistently superior performance across six public benchmark datasets.
FinCausal 2020 task detects financial document causality.
problem Detect causal relationships in financial documents.
method Binary classification and relation extraction tasks.
result 16 teams participated, 13 submitted system descriptions.
DoFlow models time series data for causal forecasting and anomaly detection.
problem Forecasting and causal reasoning in multivariate time series.
method Flow-based generative model over causal DAGs.
result Accurate interventional and counterfactual forecasting, anomaly detection.
Novel algorithm detects causal macrovariables from high-dimensional data.
problem Leveraging high-dimensional observational datasets for coarse-grained causal models.
method Inspired by information bottlenecks, novel algorithm detects macrovariables and investigates causal relationships through additive noise models.
result Algorithm robustly detects and infers causal relationships in both synthetic and real climate datasets.
Alexander quandles fail to distinguish certain links, thus not detecting causality.
problem Detecting causality in spacetimes using link polynomials.
method Examined Alexander quandles' ability to distinguish specific links.
result Alexander quandles cannot distinguish the connected sum of two Hopf links and Allen-Swenberg Links.
Detects change points in time series focusing on specific components.
problem Identifying moments when specific components of multivariate time series change distributions.
method Two-stage non-parametric algorithm: causal structure learning followed by change point detection.
result Validated the approach on synthetic and real-world datasets.
Detects model misspecifications in causal models using observational data.
problem Identifying predictor variables with causal effects in misspecified models.
method Develops a general framework based on observational data distribution and proposes an algorithm for finite sample data.
result Identifies predictor variables for causal effects even in misspecified models.
CMC method detects causal relationships in time series data.
problem Understanding causal relationships in nonlinear systems.
method Cross-Mapping Coherence method, based on nonlinear state-space reconstruction and coherence metrics.
result CMC accurately identifies causal connections in various systems.
Algorithm detects causal change points quickly with adaptive interventions.
problem Detecting changes in causal models with interventions.
method Centralization technique, Kullback-Leibler divergence for intervention selection, adaptive intervention policy.
result Theoretical first-order optimality and validation through simulations and real-world studies.
LLMs detect market patterns through causal reasoning, not just temporal association.
problem Detecting structural market patterns in financial data.
method Obfuscation testing using the WHO-WHOM-WHAT framework.
result LLMs achieve 71.5% detection rate of market patterns without temporal context.
Detects causal scenarios with inequality constraints among classical correlations.
problem Classifying causal structures and identifying those with inequality constraints.
method Using d-separation, e-separation, incompatible supports, and HLP condition.
result Resolved all but three causal scenarios with up to 4 observed variables.
New method detects structural shifts in multivariate Hawkes processes.
problem Detecting changes in multivariate Hawkes processes.
method Using Fréchet statistics on overlapping windows of causal network.
result Accurately detects and characterizes changes in causal structure.
Information transfer between time series is calculated by using the asymmetric information-theoretic measure known as transfer entropy. Geweke's autoregressive formulation of Granger causality is used to find linear transfer entropy, and Schreiber's general, non-parametric, information-theoretic formulation is used to …
SAGE-FIN detects financial fraud using GNNs and Granger causality.
problem Detecting fraud in financial networks with limited labeled data and lack of explainability.
method Semi-supervised GNN approach with Granger causal explanations.
result SAGE-FIN outperforms on real-world financial network dataset with explainable flagged items.
Detect spacetime curvature with event causality measurements.
problem Detecting spacetime curvature without rulers and clocks.
method Prove spacetime non-flatness through causal relations.
result Sixteen measurements verify non-flatness of non-conformally flat spacetimes.
The study analyzes and mitigates errors in PC-based causal discovery methods.
problem Errors in PC-based causal discovery methods can lead to incorrect graphs.
method The study introduces coherency scores to detect assumption violations and small sample errors in PC-based methods.
result The coherency scores can detect errors that other methods cannot, bridging between global and local error detection.
InGRA models for efficient Granger causality learning in multivariate time series.
problem Efficiently modeling Granger causality in large-scale multivariate time series data.
method Inductive GRanger causal modeling (InGRA) framework with prototypical Granger causal attention.
result InGRA detects common causal structures and infers Granger causal structures for new individuals.
We observe that Khovanov homology detects causality in (2+1)-dimensional globally hyperbolic spacetimes whose Cauchy surface is homeomorphic to R2
New method detects bias in AI models that generate data.
problem Detecting bias in AI models that generate data.
method Formalized causal fairness in generative AI, derived new decomposition results, established identification conditions, and introduced efficient estimators.
result Demonstrated the value of new methodology in analyzing bias in large language models.
CARD detects treatment responders with machine learning and adjustment.
problem Identifying responders in non-random treatment settings.
method Conformal prediction, machine learning, propensity score adjustment.
result High power responder detection in various scenarios.
New method falsifies causal discovery results without ground truth.
problem Evaluation of causal discovery algorithms without ground truth data.
method Detects incompatibilities between causal graphs learned on different subsets of variables.
result Detection of incompatibilities can falsify wrongly inferred causal relations.
We describe a method that infers whether statistical dependences between two observed variables X and Y are due to a "direct" causal link or only due to a connecting causal path that contains an unobserved variable of low complexity, e.g., a binary variable. This problem is motivated by statistical genetics. Given a ge…
Paper tackles anomaly detection and RCA in dynamical systems using ICODE Networks.
problem Anomalies in dynamical systems impact performance and reliability.
method Proposes ICODE Networks for anomaly detection, RCA, and type classification.
result Demonstrates the ability to accurately detect anomalies, classify types, and pinpoint origins.
The cost of wind energy can be reduced by using SCADA data to detect faults in wind turbine components. Normal behavior models are one of the main fault detection approaches, but there is a lack of consensus in how different input features affect the results. In this work, a new taxonomy based on the causal relations b…
Framework detects anomalies in industrial processes using deep learning.
problem Detect anomalies in complex industrial processes.
method Causal-based framework with unsupervised deep learning.
result Successfully validated abstract contexts of blast furnace assets.
New method detects currency contagion sources using causal inference.
problem Lack of causal interpretation in quantifying contagion among currencies.
method Network-based causal inference to identify contagion paths.
result Identifies sources of contagion and diversification options.
The Links--Gould polynomial detects causality in spacetimes.
problem Detecting causality in spacetimes using link invariants.
method Using the Links--Gould polynomial, which specializes to the Alexander--Conway polynomial.
result The Links--Gould polynomial distinguishes Allen-Swenberg links and detects causality.
While most classical approaches to Granger causality detection repose upon linear time series assumptions, many interactions in neuroscience and economics applications are nonlinear. We develop an approach to nonlinear Granger causality detection using multilayer perceptrons where the input to the network is the past t…
Online algorithm detects and removes spurious features for strong generalization.
problem Predictive models learn spurious correlations from incomplete data.
method Online continual learning algorithm that detects and removes spurious features.
result Algorithm converges to solutions with strong generalization.
OracleAD detects multivariate time series anomalies without labels.
problem Rare and unlabeled multivariate time series anomalies.
method OracleAD encodes past sequences into causal embeddings, projects them into a latent space, and identifies anomalies based on deviations from a stable latent structure.
result OracleAD achieves state-of-the-art results and is interpretable.
Study detects unlawful insider trading using SHAP and CF, identifying key features.
problem Detecting and explaining unlawful insider trading with MNPI.
method Combining Shapley Values and Causal Forest approaches.
result Identifies key features explaining unlawful insider trading.
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable. In pursuit of such models, we propose a causal regularizer to steer predictive models towards causally-interpretable solutions and theoretically study its properties. In a large-scale analysis of Electron…
A method to detect spillover effects and select valid donors for synthetic control models.
problem Identifying valid donors in synthetic control models when spillover effects are possible.
method Theoretical grounding and practical method using pre-intervention data to identify donor values and debias causal estimates.
result A Theorem that identifies assumptions for identifying donor values and debias causal estimates.
Medial quandles fail to distinguish certain links, highlighting their limitations.
problem Detecting causality in spacetime using quandles.
method Investigated medial quandles' ability to distinguish specific links and knots.
result Medial quandles fail to distinguish certain links, including the connected sum of two Hopf links from an infinite series of relevant three-component links.
Method detects critical events in complex systems by learning latent causal structure.
problem Detecting onset of epileptic seizures, customer churn, or pandemics from hidden causal interactions.
method A machine learning method that learns an optimal feature representation from powers of the empirical covariance or precision matrix.
result Proves structural consistency and demonstrates competitive results in seizure and churn prediction.
Detect hidden confounding in observational data using multiple environments.
problem Detect hidden confounding in observational data.
method Theoretical framework and simulation studies to test for hidden confounding.
result The proposed procedure correctly predicts hidden confounding, especially when bias is large.
CNMs detect tipping points in complex systems using causal network markers.
problem Identifying tipping points ahead of critical transitions in complex systems.
method Introducing CNMs that incorporate causality indicators to detect tipping points.
result CNMs show higher predictive power and accuracy than traditional DNB indicators.
Improved Granger causality method for dynamic time series data.
problem Traditional Granger causality method assumes constant causalities, failing to model dynamic causalities.
method Dynamic window-level Granger causality (DWGC) method with causality indexing.
result Improved DWGC method better detects window-level causalities.
While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of non…
Enhanced symplectic quandle colorings detect causal structure in spacetime diagrams.
problem Detecting causal structure in spacetime diagrams using polynomial invariants.
method Comparing symplectic quandle colorings of different diagrams representing spacetime connections.
result Enhanced symplectic quandle colorings consistently distinguish between causally unrelated and related spacetime configurations.