We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied especially in the bivariate setting, the majority of current methods assume a linear causal relationship, and the few methods which consider…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,932 papers · 148 categories
Trend · papers per month
48 results for “non-linear causality”
New method recovers causal graphs from data scores in non-linear models.
problem Recovering causal graphs from data scores in non-linear models.
method Score matching algorithms and efficient Jacobian approximation.
result New method, SCORE, is competitive and faster than state-of-the-art methods.
Information-theoretic measures for non-linear causality detection: application to social media sentiment and cryptocurrency pricesphysics.data-an
Detects non-linear causality between social media sentiment and cryptocurrency prices.
problem Detecting causality between non-linear time series data.
method Use of transfer entropy to measure information transfer, validating against synthetic data, and applying significance tests.
result Significant non-linear causality detected, orders of magnitude greater than linear causality.
Proposes using DII to identify non-linear causal relationships in EU Allowances returns.
problem Identifying causal relationships in non-linear data of EU Allowances returns.
method Uses Differentiable Information Imbalance (DII) for non-parametric causal discovery compared to multivariate Granger causality.
result Significant overlap and differences in causal variables identified by linear and non-linear methods.
We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities. We introduce σ-connection graphs (σ-CG), a new class of mixed graphs (containing undirected, bidirected…
New method for learning causal relationships in PNL models.
problem Learning causal relationships from empirical observations in PNL models.
method Rank-based methods to estimate non-linear functions, disentangling from independence tests.
result Consistent method for PNL causal discovery, validated in experiments.
Estimates causal effects using neural autoregressive density estimators.
problem Estimating causal effects in non-linear systems.
method Neural autoregressive density estimators within Pearl's do-calculus framework.
result Retrieves causal effects from non-linear systems without explicit modeling.
Deep learning aids causal inference in complex settings.
problem Estimating heterogeneous treatment effects in non-linear, time-varying, and encoded confounders.
method Intuitive introduction to deep learning and causal inference, focusing on observational data.
result Maximizes accessibility to causal inference through deep learning.
Bayesian method estimates intervention effects in non-linear data.
problem Causal discovery from observational data with non-linear relationships.
method Gaussian Process Networks (GPN) with Bayesian estimation and Monte Carlo methods.
result Approach accurately identifies and reflects uncertainty of causal estimates.
Causal normalizing flows recover causal models from observational data.
problem Recovering causal models from observational data.
method Use autoregressive normalizing flows and analyze design choices.
result Causal normalizing flows can capture causal data-generating processes.
DECI combines causal discovery and inference in a single model for diverse data types.
problem Combining causal discovery and inference methods for diverse data types.
method Develops a single flow-based non-linear additive noise model (DECI) for causal discovery and inference.
result DECI can recover ground truth causal graphs and perform (C)ATE estimation.
The Trek Separation Theorem (Sullivant et al. 2010) states necessary and sufficient conditions for a linear directed acyclic graphical model to entail for all possible values of its linear coefficients that the rank of various sub-matrices of the covariance matrix is less than or equal to n, for any given n. In this pa…
CausalMan simulates complex causality for fair benchmarking.
problem Lack of realistic causal models with known data generating processes.
method Developed a physics-based simulator, CausalMan, for large-scale causality.
result Demonstrated inadequacy of state-of-the-art approaches and analyzed their performance.
Wiener-Granger causality is a widely used framework of causal analysis for temporally resolved events. We introduce a new measure of Wiener-Granger causality based on kernelization of partial canonical correlation analysis with specific advantages in the context of large high-dimensional data. The introduced measure is…
Least-Squares Independence Regression for Non-Linear Causal Inference under Non-Gaussian Noisestat.ML
The discovery of non-linear causal relationship under additive non-Gaussian noise models has attracted considerable attention recently because of their high flexibility. In this paper, we propose a novel causal inference algorithm called least-squares independence regression (LSIR). LSIR learns the additive noise model…
This study uses causal Shapley values to analyze how socioeconomic factors cause the spread of COVID-19.
problem Understanding how socioeconomic factors cause the spread of COVID-19.
method The study employs an explanatory framework from cooperative game theory augmented with do calculus, specifically causal Shapley values, to analyze the causal connections.
result The causal Shapley values reveal distinct advantages of non-linear machine learning models over linear models in multivariate analysis.
New algorithms predict causal links better than traditional methods in time series data.
problem Learning causal structure from time series data with challenges in real-world Earth sciences.
method Combination of established ideas for linear methods to identify causal links in non-linear systems, with a focus on large regression coefficients.
result Large regression coefficients can predict causal links better than small p-values in practice.
In our previous study we have presented an approach to studying lead--lag effect in financial markets using information and network theories. Methodology presented there, as well as previous studies using Pearson's correlation for the same purpose, approached the concept of lead--lag effect in a naive way. In this pape…
Combining neural networks and multiscale decomposition for financial market analysis.
problem Financial markets' complexity and mainstream models' limitations in capturing non-linear structures.
method Neural networks for non-linear associations combined with multiscale decomposition.
result Improved understanding of financial market data substructures.
Paper introduces VBG for Bayesian causal structure and mechanism learning.
problem Bayesian causal structure learning with uncertainty over models.
method Variational Bayes-DAG-GFlowNet (VBG) method.
result VBG outperforms existing methods in modeling posterior over DAGs and mechanisms.
New method discovers causal relationships in complex time series data.
problem Discovering causal relationships in multivariate time series is challenging.
method Temporal Dependency to Causality (TD2C) framework using mutual information.
result TD2C achieves state-of-the-art performance in causal discovery.
SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modelingstat.ML
SLEM uses machine learning to improve causal inference from observational data.
problem Improving causal inference from observational data using non-linear relationships.
method Super Learner Equation Modeling integrating machine learning ensembles.
result SLEM provides consistent and unbiased estimates of causal effects.
Rhino learns causal relationships from time series data with history-dependent noise.
problem Discovering causal relationships from time series data with non-linear relations, instantaneous effects, and history-dependent noise.
method Combines vector auto-regression, deep learning, and variational inference.
result Demonstrates better causal relationship discovery performance compared to baselines.
We provide theoretical and empirical evidence for a type of asymmetry between causes and effects that is present when these are related via linear models contaminated with additive non-Gaussian noise. Assuming that the causes and the effects have the same distribution, we show that the distribution of the residuals of …
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…
New neural network models improve Granger Causality detection in non-linear systems.
problem Mischaracterization of Granger Causality in non-linear systems using traditional linear models.
method Proposes Learned Kernel VAR (LeKVAR) and decoupled penalties for GC estimation and lag selection.
result Improves GC detection in non-linear systems with computational efficiency.
Deep Causal Graphs model complex causal relationships using neural networks.
problem Limited applicability of parametric causal models to real-life datasets with non-linear relationships.
method Deep Causal Graphs, an abstract specification for neural networks to model causal distributions.
result Demonstrates expressive power in modelling complex interactions and provides true causal counterfactuals.
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confoundersstat.ML
Method learns causal effects from multiple interventions in presence of unobserved confounders.
problem Disentangling causal effects from sets of interventions in the presence of unobserved confounders.
method Non-linear structural causal models with additive, multivariate Gaussian noise; algorithm that learns causal model parameters by pooling data from different regimes and maximizing combined likelihood.
result Identification proofs demonstrate that causal effects of single interventions can be learned from sets of interventions, even with unobserved confounders.
DeepCausalMMM: A Deep Learning Framework for Marketing Mix Modeling with Causal Structure Learningcs.LG
DeepCausalMMM models marketing impacts using deep learning and causal inference.
problem Traditional MMM approaches struggle with non-linear dynamics and temporal patterns.
method Combines deep learning, causal inference, and marketing science. Uses GRUs for temporal patterns and DAG structure for channel dependencies.
result Captures non-linear dynamics and temporal patterns in marketing impacts.
FSNN learns causal relationships in complex systems using neural nets.
problem Inferring causality in directed cyclic graphs.
method Constructs a non-linear system of ODEs using feed forward neural nets.
result Accurately models complex, non-linear systems with causal relationships.
Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals reco…
New method discovers causal relationships in large-scale data.
problem Discovering causal relationships in large datasets with thousands of variables.
method Factor Directed Acyclic Graphs (f-DAGs) combined with continuous optimization.
result Achieved causal discovery on thousands of variables.
We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal models that allow for cycles, latent confounders and arbitrary probability distributions. We also generalize adjustment c…
Estimates Granger causality with unobserved confounders using deep latent-variable recurrent neural networks.
problem Non-linear Granger causality with unobserved confounders in observational studies.
method Generative model with latent variable, variational autoencoder, recurrent neural network.
result Estimated confounders improve performance in non-linear Granger causality with multiple proxies.
New method infers causal relationships from nonstationary time series data.
problem Challenges in inferring causal relationships from nonstationary time series data.
method Proposes a new class of restricted SCM with time-varying filters and stationary noise, leveraging asymmetry from nonstationarity.
result Demonstrates effectiveness of the proposed methodology on various synthetic and real datasets.
DISTANA predicts and denoises spatial wave dynamics.
problem Identifying causality in spatially distributed, non-linear dynamical processes.
method Generative, recurrent graph convolution neural network architecture (DISTANA).
result DISTANA outperforms alternative approaches in denoising and predicting complex spatial wave propagation.
This paper analyzes the direction of the causality between crude oil, gold and stock markets for the largest economy in the world with respect to such markets, the US. To do so, we apply non-linear Granger causality tests. We find a nonlinear causal relationship among the three markets considered, with the causality go…
Bayesian optimization selects experiments for causal structure learning in Gaussian process networks.
problem Discover causal relationships in non-linear systems with continuous variables.
method Bayesian active learning and Gaussian process priors combined with Bayesian optimization for experiment selection.
result Efficiently maximizes expected information gain in learning causal structure.
Framework for Granger causality in extreme events.
problem Identifying causal links from extreme events in time series.
method Causal tail coefficient and novel inference method.
result Framework outperforms state-of-the-art methods in detecting Granger causality in extremes.
Bayesian causal inference method improves accuracy over traditional approaches.
problem Bayesian marginalisation over causal models is computationally infeasible.
method Decomposes structure marginalisation into causal orders and DAGs, using Gaussian processes for mechanisms and ARCO for orders.
result Method outperforms state-of-the-art in structure learning and inference.
Proposes a method to infer causal effects from incomplete data using latent confounders.
problem Missing data complicates causal inference, especially for non-linear models.
method Uses variational autoencoders to learn latent confounders and incorporate missing values.
result Demonstrates effectiveness of the method, especially for non-linear models.
Framework generates causal probabilities from observational data.
problem Generating causal probabilities from observational data.
method Moment-matching graph-networks for causal inference.
result Automated sampling of latent space conditional probability distributions.
NO-BEARS algorithm speeds up gene network inference from transcriptomic data.
problem Constructing accurate gene regulatory networks from transcriptomic data.
method NO-BEARS algorithm, based on NOTEARS, with new constraint and polynomial regression loss.
result Significantly reduced computational time and improved accuracy in inferring gene regulatory networks.
DiD-BCF model improves causal inference in panel data with robust non-parametric methods.
problem Challenges in Difference-in-Differences (DiD) estimation, especially heterogeneous treatment effects and non-linearities.
method Difference-in-Differences Bayesian Causal Forest (DiD-BCF) with PTA-based reparameterization.
result DiD-BCF provides superior performance and uncovers significant heterogeneity in treatment effects.
Study shows how financial report sentiment impacts bank profitability.
problem Understanding causal effects of financial report sentiment on bank profitability.
method Causal forest machine learning methodology, FinancialBERT sentiment scores, SHAP analysis, comprehensive dataset.
result Statistically significant causal associations between balance sheet and expense management variables and profitability.
CausalRegNet generates accurate data for gene perturbation experiments, improving CSL methods.
problem Assessing and selecting causal structure learning methods in gene perturbation experiments.
method CausalRegNet, a multiplicative effect structural causal model, generates accurate observational and interventional data.
result CausalRegNet generates more accurate distributions and scales better than current simulation frameworks.
New algorithm discovers causal graphs efficiently from observational data.
problem Discovering causal graphs from observational data efficiently.
method Approximating the score function using machine learning and applying scalable techniques.
result DAS algorithm reduces complexity and achieves competitive accuracy.
Causal Deep Learningcs.LG
Causal deep learning tackles causal inference using tensor factor analysis.
problem Addressing causal questions in data using neural networks.
method Tensor factor analysis and neural network architectures (causal capsules, tensor transformer, multilinear projection algorithm).
result Derives deep neural networks for causal inference with tensor factor analysis.