We formulate the generalization of the Legendrian Low conjecture of Natario and Tod (proved by Nemirovski and myself before) to the case of causally simple spacetimes. We prove a weakened version of the corresponding statement. In all known examples, a causally simple spacetime can be conformally embedded as a…
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.
168,694 papers · 148 categories
Trend · papers per month
48 results for “causally simple”
It is shown that the space of null geodesics of a star-shaped causally simple subset of Minkowski space is contactomorphic to the canonical contact structure in the spherical cotangent bundle of . In the -dimensional case we prove a similar result for a large class of causally simple contractible subse…
It is shown that the space of null geodesics of a causally simple Lorentzian manifold is Hausdorff if it admits an open conformal embedding into a globally hyperbolic spacetime. This provides an obstruction to conformal embeddings of causally simple spacetimes into globally hyperbolic ones irrespective of curvature con…
A simple guide to understanding hierarchical causality in complex systems.
problem Understanding hierarchical causality in complex systems.
method Formalizing hierarchical causality in terms of actors and agents, with three key structures.
result The system requires three additional structures: causation classes, aggregation operators, and discrete event-time maps.
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutationstat.ML
Proposes a new classifier for causal discovery in categorical data.
problem Causal discovery for categorical data.
method Classification with optimal label permutation (COLP) and simple learning algorithm.
result Favorable performance compared to state-of-the-art methods.
A simple characterization of the causal automorphisms of 1+1 Minkowski spacetime is given.
The paper proves a conjecture about spacetimes and singularities.
problem Understanding naked singularities and causally simple spacetimes.
method Analyzes null geodesics and spacetime properties to prove conjectures.
result Proves a conjecture about spacetimes and singularities, including implications for two-dimensional spacetimes.
Continuous Lorentzian metrics yield infinitesimal Minkowskian spacetimes.
problem Understanding spacetime properties from continuous Lorentzian metrics.
method Proving infinitesimal Minkowskianity for causally simple metric measure spacetimes.
result Continuous Lorentzian metrics result in spacetimes that are infinitesimally Minkowskian.
Paper axiomatizes interventional probability distributions.
problem Causal inference and intervention.
method Axiomatization of interventional families.
result Markovian property of intervened distributions.
Defines explanations for classifier outcomes using causal concepts.
problem Understanding classifier outcomes in a causal context.
method Proposes a new definition of explanation based on causality, compares it with existing notions, and evaluates it experimentally.
result Experimental evaluation shows the new definition's effectiveness on financial datasets.
We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm t…
New criteria distinguish cause from effect in data, overcoming statistical limitations.
problem Determining causal direction from statistical dependence alone.
method Intuitive criteria based on simplicity of prediction, tested on synthetic data.
result Criteria accurately distinguish cause from effect in various scenarios.
Study open orbits in causal flag manifolds with applications in AQFT.
problem Understanding open orbits in causal flag manifolds for applications in AQFT.
method Analyzing open orbits of symmetric subgroups on causal flag manifolds, focusing on invariant causal structures and modular flows.
result Determine the positivity regions of modular flows and their global hyperbolicity for different types of open orbits.
Weak supervision enables learning causal representations from unstructured data.
problem Learning high-level causal representations from unstructured data like images.
method Weakly supervised setting with paired samples before and after interventions. Implicit latent causal models using variational autoencoders.
result Models can reliably identify causal structure and disentangle causal variables.
Structural Causal Models (SCMs) provide a popular causal modeling framework. In this work, we show that SCMs are not flexible enough to give a complete causal representation of dynamical systems at equilibrium. Instead, we propose a generalization of the notion of an SCM, that we call Causal Constraints Model (CCM), an…
Theory and methods to mitigate omitted variable bias in causal machine learning.
problem Mitigating omitted variable bias in causal machine learning models.
method Developed a general theory and flexible statistical inference methods for bounding and testing the magnitude of omitted variable bias.
result Simple plausibility judgments can bound the magnitude of omitted variable bias in complex, nonlinear models.
We solve structure learning for cyclic linear causal models using observational data.
problem Learning the structure of cyclic linear causal models from observational data.
method Assuming simple graphs, we use a criterion for distributional equivalence and implement a greedy search method.
result We show that simple cyclic models are of expected dimension and justify score-based methods for structure learning.
SCBMs model causal effects using low-dimensional bottlenecks.
problem Causal effect estimation in high-dimensional systems.
method Structural causal models with low-dimensional summary statistics.
result SCBMs provide a flexible framework for task-specific dimension reduction.
The paper explores Wiener-Granger causality and its computational enhancements.
problem Analyzing causal relationships between time series data.
method Detailed overview of Granger causality, historical development, and computational advancements.
result Enhanced application of Granger causality in various fields.
We construct and identify star representations canonically associated with holonomy reducible simple symplectic symmetric spaces. This leads the a non-commutative geometric realization of the correspondence between causal symmetric spaces of Cayley type and Hermitian symmetric spaces of tube type.
New technique learns causally disentangled representations for better generation.
problem Learning disentangled representations for accurate generation.
method Causally Disentangled Generation (CDG) approach with supervised regularization.
result CDG is necessary and sufficient for accurate disentangled generation.
We study the performance of Local Causal Discovery (LCD), a simple and efficient constraint-based method for causal discovery, in predicting causal effects in large-scale gene expression data. We construct practical estimators specific to the high-dimensional regime. Inspired by the ICP algorithm, we use an optional pr…
New approach for causal inference with interdependent, time-varying latent confounders.
problem Estimating causal effects with interdependent, time-varying latent confounders.
method Variational estimation with a representer theorem and random input space.
result Demonstrates effectiveness on various temporal datasets.
We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified. This is in some sense the second step after causal discovery. Taking a probabilistic approach to estimating these functions, we derive a natural myopic act…
grangersearch: An R Package for Exhaustive Granger Causality Testing with Tidyverse Integrationstat.CO
grangersearch tests causal relationships in time series data.
problem Testing causal relationships between multiple time series.
method Exhaustive pairwise search, automatic lag order optimization, tidyverse integration.
result Automated Granger causality testing simplifies causal analysis.
Unified techniques improve stability and replicability in changing data.
problem Concept drift in data generating distribution.
method Removing hidden confounding and causal regularization.
result Improves stability, replicability, and robustness in heterogeneous data.
Generalizes causal inference to high-dimensional outcomes.
problem Limited causal inference methods for multivariate outcomes.
method Formulates causal discrepancy tests for nominal variables, uses conditional independence tests.
result Causal CDcorr method improves finite sample validity and power.
The estimation of optimal treatment regimes is of considerable interest to precision medicine. In this work, we propose a causal -nearest neighbor method to estimate the optimal treatment regime. The method roots in the framework of causal inference, and estimates the causal treatment effects within the nearest neig…
Method estimates bivariate causal models using normalising flows and variational Gaussian process regression.
problem Lack of explainability in AI models, especially in causal mechanisms.
method Combination of normalising flows for density estimation and variational Gaussian process regression for post-nonlinear models.
result Method better explains cause-effect pairs than simple additive noise models.
It is postulated that quantum gravity is a sum over causal structures coupled to matter via scale evolution. Quantized causal structures can be described by studying simple matrix models where matrices are replaced by an algebra of quantum mechanical observables. In particular, previous studies constructed quantum grav…
New method reduces bias in estimating causal effects from discretized variables.
problem Bias in estimating causal effects from discretized continuous variables.
method Proposes a bias-reduced functional that evaluates outcome regression at within-bin conditional means.
result Demonstrates substantial bias reduction and near-nominal confidence interval coverage.
Ordinal Causal Discoverystat.ME
New method uniquely identifies causal structure from ordinal data.
problem Challenges in causal discovery for categorical data, especially direction of relationships.
method Exploits ordinal information to uniquely identify causal structure.
result Favorable and robust performance compared to state-of-the-art methods.
Causal Autoregressive Flowsstat.ML
Causal autoregressive flows enable accurate causal inference and prediction.
problem Causal discovery and interventional predictions in machine learning.
method Autoregressive normalizing flows with fixed variable orderings.
result Causal models derived from autoregressive flows are identifiable and allow for accurate interventional and counterfactual predictions.
Simple linear models reveal complex cryptocurrency networks.
problem Understanding complex causal networks in cryptocurrency markets.
method Multivariate linear models to infer financial networks from cryptocurrency price series.
result Simple linear models can create informative cryptocurrency networks reflecting economic intuition.
Granger causality reviewed and advanced for complex data.
problem Validity of inferring causal relationships from time series data.
method Recent advances in models for high-dimensional time series, accounting for nonlinear and non-Gaussian observations, and sub-sampled data.
result Improved computational tools for Granger causality.
New kernel methods estimate complex causal relationships.
problem Estimating nonparametric causal functions like dose-response curves.
method Kernel ridge regression with decomposition property.
result Uniform consistency with finite sample rates proved.
Caus-Modens uses deep ensembles to better predict causal outcomes in hidden confounding scenarios.
problem Predicting causal outcomes in the presence of hidden confounders.
method Caus-Modens employs a modulated ensemble approach to improve prediction intervals for causal outcomes using sensitivity models.
result Caus-Modens provides tighter prediction intervals for causal outcomes compared to existing methods.
A rank-n tensor on a Lorentzian manifold V whose contraction with n arbitrary causal future directed vectors is non-negative is said to have the dominant property. These tensors, up to sign, are called causal tensors, and we determine their general properties in dimension N. We prove that rank-2 tensors which map the n…
Inferring the causal structure that links n observables is usually based upon detecting statistical dependences and choosing simple graphs that make the joint measure Markovian. Here we argue why causal inference is also possible when only single observations are present. We develop a theory how to generate causal grap…
Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learningcs.LG
The study uncovers latent capabilities of language models via causal representation learning.
problem Rigorous causal evaluations of language model capabilities are challenging due to confounding effects and computational costs.
method Proposes a causal representation learning framework to identify latent capability factors as causally interrelated after controlling for a common confounder (base model).
result Identifies a three-node linear causal structure explaining performance variations across 1500 models and six benchmarks.
ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effectscs.LG
Proposes a new Alzheimer's disease simulator for causal effect estimation.
problem Lack of suitable benchmarks for evaluating causal effect estimators in real-world healthcare data.
method Developed a simulator of Alzheimer's disease using ADNI dataset, incorporating various parameters to model complexities.
result Compared estimators of average and conditional treatment effects using the new simulator.
New causal models for growing networks avoid node deletion constraints.
problem Statistical models based on node exchangeability are not suitable for growing networks.
method Enumerated and partitioned causal directed acyclic graph (DAG) models over pairs of nodes.
result Simple model exhibits flexible power-law degree distributions and emergent phase transitions.
Study on merging predictors in causal and anticausal directions using CMAXENT.
problem Comparing merging predictors in causal and anticausal directions.
method Using CMAXENT as inductive bias, study differences in merging predictors.
result CMAXENT solution reduces to logistic regression in causal direction and LDA in anticausal direction.
Study interpolating estimators for causal learning from observational data.
problem Learning causal models from observational data in complex model classes.
method Investigate min-norm interpolators and ridge-regularized regressors in a linearly confounded model.
result Interpolators cannot be optimal for causal learning under the principle of independent causal mechanisms, requiring stronger regularization.
New model learns causal world dynamics from state space models.
problem Lack of causal world models in neural world modeling.
method State Space Models (SSM) with attention mechanisms.
result SSM can learn causal models of environments with equivalent performance.
The paper discusses selecting predictive models for causal inference, highlighting the challenges and proposing a solution.
problem Selecting the best predictive models for causal inference from a variety of machine learning models.
method The paper proposes using , flexible estimators, and splitting data to compute risks for model selection.
result The proposed method controls both outcome errors for treated and non-treated individuals, addressing the issue of model selection for causal inference.
Finslerian lightconvex boundaries: applications to causal simplicity and the space of cone geodesics math.DG
New findings on lightconvex boundaries in Finslerian spacetimes.
problem Understanding causal structures in Finslerian spacetimes.
method General results for indefinite Finslerian manifolds with boundary, and equivalence among boundary lightconvexity, causally simple interior, and Hausdorff space of cone geodesics.
result Equivalence among boundary lightconvexity, causally simple interior, and Hausdorff space of cone geodesics in globally hyperbolic spacetimes.
TRAM-DAG models bridge interpretability and flexibility in causal modeling.
problem Modeling causal relationships in diverse data types while maintaining interpretability.
method Using transformation models (TRAMs) within structural causal models (SCMs) to handle various data types and maintain interpretability.
result TRAM-DAG models achieve equal or superior performance in causal queries across different levels of the causal hierarchy.