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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.

168,695 papers · 148 categories

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48 results for “uncertain causality”

Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learningcs.LG

Meta-learning model predicts intervention effects from uncertain causal graphs.

problem Estimating intervention effects when causal structures are uncertain.

method Model-Averaged Causal Estimation Transformer Neural Process (MACE-TNP) using meta-learning.

result MACE-TNP outperforms Bayesian baselines in predicting intervention distributions.

meta-learning causal inference interventional distributions uncertain graphs neural processes

2025-07-07abs ↗pdf ↗

Model-Free and Model-Based Policy Evaluation when Causality is Uncertaincs.LG

New bounds assess policy evaluation under unobserved confounders, showing model-based methods are more effective.

problem Policy evaluation under unobserved confounders in uncertain causal environments.

method Developed worst-case bounds for sensitivity to unobserved confounders, demonstrating model-based methods are more effective.

result Model-based approaches with robust MDPs provide sharper lower bounds for policy evaluation.

policy evaluation uncertain causality confounders model-based methods robust MDPs

2022-04-02abs ↗pdf ↗

Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularizationmath.ST

Paper shows hard computational limits for invariant causal prediction.

problem Hard computational limits for invariant causal prediction.

method Distributionally robust estimator with ellipse-shaped uncertain set.

result Estimation error rate can be arbitrarily slow for computationally efficient algorithms.

invariant causal prediction NP-hard distributionally robust elliptical uncertain set computational limits

2025-01-29abs ↗pdf ↗

A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderationstat.ME

Meta-learning method for estimating time-varying mHealth intervention effects.

problem Complex mHealth data and uncertain randomization probabilities.

method DR-WCLS meta-learning procedure for causal excursion effects.

result More efficient and consistent estimates of causal excursion effects.

meta-learning causal excursion effects mHealth interventions time-varying effects wearable technologies

2023-06-28abs ↗pdf ↗

BaCaDI: Bayesian Causal Discovery with Unknown Interventionscs.LG

BaCaDI discovers causal structures from unknown interventions.

problem Inferring causal structures from unknown interventions with limited data.

method Bayesian framework with gradient-based variational inference.

result BaCaDI outperforms related methods in identifying causal structures and intervention targets.

causal inference Bayesian methods unknown interventions gradient-based inference single-cell data

2022-06-03abs ↗pdf ↗

Causal Effect Inference with Deep Latent-Variable Modelsstat.ML

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affec…

2017-05-24abs ↗pdf ↗

From Causal Discovery to Dynamic Causal Inference in Neural Time Seriescs.LG

Proposes DCNAR for dynamic causal inference from neural time series.

problem Uncertainty and evolution of causal structure in real-world domains.

method Two-stage neural causal modeling integrating discovery and inference.

result Dynamic causal inferences are more stable and meaningful than alternatives.

dynamic causal inference neural time series causal discovery AI as scientific instrument structural uncertainty

2026-03-21abs ↗pdf ↗

Expert-Aided Causal Discovery of Ancestral Graphscs.LG

AGFN improves causal discovery by integrating expert feedback and handling latent confounding.

problem Inaccurate causal discovery due to unreliable expert knowledge and latent confounding.

method Ancestral GFlowNet (AGFN) is a reinforcement learning algorithm that iteratively refines a policy based on noisy expert feedback to infer ancestral graphs.

result AGFN converges to the true ancestral graph given accurate expert responses and outperforms baselines in structural Hamming distance and Bayesian Information Criterion.

causal discovery ancestral graphs reinforcement learning expert feedback latent confounding

2023-09-21abs ↗pdf ↗

Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Inputq-fin.ST

Unified framework predicts S&P500 index direction using transfer learning and causal graph.

problem Predicting the movement of financial indices like S&P500.

method Transfer learning, causal graph, multidisciplinary knowledge, VAE network.

result 74.3% accuracy, 67% F1-score, 0.42 Matthew correlation on 12 years test period.

transfer learning causal graph financial markets S&P500 index prediction

2020-11-26abs ↗pdf ↗

Uncertain Evidence in Probabilistic Models and Stochastic Simulatorsstat.ML

The paper explores how to handle uncertain evidence in probabilistic models.

problem Handling uncertain evidence in probabilistic models and stochastic simulators.

method The paper considers distributional evidence, Jeffrey's rule, and virtual evidence as methods for interpreting uncertain evidence.

result The paper provides guidelines on how to account for uncertain evidence and highlights the importance of careful consideration.

Bayesian inference uncertain evidence distributional evidence Jeffrey's rule virtual evidence

2022-10-21abs ↗pdf ↗

Shapley Values with Uncertain Value Functionscs.LG

New method calculates Shapley values for uncertain functions.

problem Uncertain value functions in explainable machine learning.

method Definition of Shapley values using probability theory.

result Shapley values can be applied to uncertain functions.

Shapley values uncertain value functions explainable machine learning probability theory

2023-01-19abs ↗pdf ↗

Valuation of equity warrants for uncertain financial marketq-fin.PR

In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…

2017-11-22abs ↗pdf ↗

A Perspective on Gaussian Processes for Earth Observationcs.LG

Gaussian processes enhance EO with accurate and uncertain predictions.

problem Challenges in GP models for EO, including data-driven physics and causal inference.

method Data-driven physics-aware models respecting signal characteristics and physical laws.

result GP models need to evolve for better EO applications.

Gaussian processes Earth observation remote sensing uncertainty quantification physics-aware models

2020-07-02abs ↗pdf ↗

Modelling Uncertain Volatility Using Quantum Stochastic Calculus: Unitary vs Non-Unitary Time Evolutionq-fin.MF

Quantum methods model uncertain volatility in financial markets.

problem Modeling financial asset prices with uncertain volatility.

method Quantum stochastic calculus with unitary and non-unitary time evolution.

result Different volatility levels encoded in quantum states, leading to varied market price evolutions.

quantum finance stochastic processes uncertain volatility Monte-Carlo simulations projective measurements

2024-07-05abs ↗pdf ↗

Modeling dynamic volatility under uncertain environment with fuzziness and randomnessq-fin.MF

New model predicts dynamic volatility in uncertain financial markets.

problem Predicting dynamic volatility in financial markets with uncertainty.

method Generalized Barndorff-Nielsen and Shephard (BN-S) model considering delay and fuzziness.

result Effective prediction of dynamic volatility with improved performance.

dynamic volatility uncertain environment fuzziness randomness BN-S model

2022-04-27abs ↗pdf ↗

G-consistent price system and bid-ask pricing for European contingent claims under Knightian uncertaintyq-fin.PR

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …

2013-08-28abs ↗pdf ↗

Asymptotic Bayes risk of semi-supervised learning with uncertain labelingstat.ML

Study calculates Bayes risk for semi-supervised learning with uncertain labels.

problem Uncertain labeling in semi-supervised classification.

method Gaussian mixture model, Bayes risk computation, comparison with algorithm performance.

result New insights into semi-supervised learning algorithm performance.

Bayes risk semi-supervised learning uncertain labeling Gaussian mixture model

2024-03-26abs ↗pdf ↗

Uncertain Time Series Classification With Shapelet Transformcs.LG

New algorithm for uncertain time series classification.

problem Uncertainty in time series data.

method Uncertain dissimilarity measure based on Euclidean distance and uncertain shapelet transform.

result Effectiveness of the uncertain shapelet transform algorithm on state-of-the-art datasets.

time series classification uncertainty shapelet transform Euclidean distance

2021-02-03abs ↗pdf ↗

Ensuring Safety in an Uncertain Environment: Constrained MDPs via Stochastic Thresholdscs.LG

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

problem Safety in reinforcement learning in unknown and uncertain environments.

method Growing-Window estimator sampling and Stochastic Pessimistic-Optimistic Thresholding (SPOT) algorithm.

result Achieves sublinear regret and constraint violation of

ilde{\mathcal{O}}(\sqrt{T})

reinforcement learning Markov decision processes uncertain environments thresholds safety

2025-04-07abs ↗pdf ↗

Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Makingcs.LG

Bridges uplift modeling and sequential decision-making with online budget allocation.

problem Treatment allocation under budget constraints in digital advertising.

method Budget-Constrained Causal Bandits (BCCB) integrates learning, exploration, and budget pacing.

result Data-efficiency crossover: BCCB operates effectively from the first user, 3-5x lower performance variance.

causal bandits budget constraints uplift modeling sequential decision-making online learning

2026-04-28abs ↗pdf ↗

Stochastic Policy Gradient Methods in the Uncertain Volatility Modelq-fin.CP

A method for accurate pricing of multidimensional derivatives under uncertain volatility.

problem High-dimensional stochastic control problem in uncertain volatility model.

method Backward actor-critic stochastic policy gradient scheme combining DP, PPO, and neural networks.

result Accurate and efficient pricing of multidimensional derivatives compared to benchmarks.

uncertain volatility stochastic control policy gradient Monte Carlo machine learning

2026-04-01abs ↗pdf ↗

Optimal portfolio allocation with uncertain covariance matrixq-fin.PM

The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.

problem Portfolio allocation with uncertain covariance matrices.

method Calculates the expected value of CARA utility function over a distribution of covariance matrices, considering uncertainty in future returns and covariances.

result Marginalization introduces a logarithmic dependence on risk, leading to lower allocation levels for higher uncertainties.

portfolio allocation uncertain covariance CARA utility logarithmic risk mean-variance approach

2023-11-13abs ↗pdf ↗

PDE-constrained Gaussian process surrogate modeling with uncertain data locationscs.LG

Bayesian Gaussian process models handle uncertain data locations in PDE approximations.

problem Handling uncertainties in data locations for PDE approximations.

method Bayesian inference of uncertain inputs integrated into Gaussian process predictions.

result Substantial reduction in predictive uncertainties achieved through Bayesian inference.

Bayesian inference Gaussian process PDE approximation uncertain data partial differential equations

2023-05-19abs ↗pdf ↗

Non-Cooperative Games with Uncertaintyecon.TH

Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.

problem Non-cooperative games with globally uncertain parameters and no common prior.

method Mixed strategies and subjective priors, Extended Equilibrium defined by fixed-point argument.

result Existence of Extended Equilibrium under certain conditions.

Non-cooperative games Uncertain parameters Subjective priors Fixed-point theorem Regret maximization

2025-02-27abs ↗pdf ↗

Valuation of European Options under an Uncertain Market Price of Volatility Riskq-fin.PR

Model quantifies uncertainty's impact on European option prices.

problem Uncertainty in market volatility risk affects option pricing.

method Hamilton-Jacobi-Bellman framework and finite element method.

result Dependence of Delta on uncertainty is nonlinear and varied.

Uncertain market price Volatility risk Hamilton-Jacobi-Bellman equation Finite element method Uncertainty quantification

2021-05-20abs ↗pdf ↗

Uncertain Decisions Facilitate Better Preference Learningstat.ML

IDT learns human preferences from uncertain decisions, even when humans are suboptimal.

problem Learning human preferences from uncertain and suboptimal decisions.

method Inverse decision theory (IDT) framework, statistical analysis of IDT, characterizing sample complexity.

result Learning preferences is easier when decisions are more uncertain, even if humans are suboptimal.

inverse decision theory human preferences uncertain decisions sample complexity suboptimal humans

2021-06-19abs ↗pdf ↗

Data-Driven Nonparametric Robust Control under Dependence Uncertaintymath.OC

The paper tackles robust control with uncertain dependence using data-driven methods.

problem Nonparametric robust control under dependence uncertainty in multi-period stochastic systems.

method Nonparametric adaptive robust control framework using stochastic gradient descent ascent algorithm.

result The controller benefits from knowing more about the uncertain model.

robust control stochastic gradient descent uncertain dependence multi-period systems data-driven methods

2022-09-12abs ↗pdf ↗

Online Optimization and Ambiguity-based Learning of Distributionally Uncertain Dynamic Systemseess.SY

Paper proposes online optimization for uncertain systems using machine learning and DRO.

problem Optimization of uncertain dynamical systems with distributional uncertainty.

method Combines machine learning with Distributional Robust Optimization (DRO) to handle uncertainty.

result Online solutions with probabilistic regret bounds for uncertain systems.

Online Optimization Distributionally Uncertain Systems Machine Learning Distributional Robust Optimization Dissipativity Theory

2021-02-18abs ↗pdf ↗

Learning to Solve Job Shop Scheduling under Uncertaintycs.AI

This paper tackles JSSP with uncertain task durations using DRL.

problem Job Shop Scheduling Problem with uncertain task durations.

method Integrates Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL) to generate robust schedules.

result Advances DRL applications to JSSPs, enhancing generalization and scalability.

Job Shop Scheduling Deep Reinforcement Learning Uncertain Durations Graph Neural Networks Robust Scheduling

2024-03-04abs ↗pdf ↗

Optimal control of uncertain stochastic systems with Markovian switching and its applications to portfolio decisionsmath.OC

This paper first describes a class of uncertain stochastic control systems with Markovian switching, and derives an Itô-Liu formula for Markov-modulated processes. And we characterize an optimal control law, which satisfies the generalized Hamilton-Jacobi-Bellman (HJB) equation with Markovian switching. Then, by using …

2014-01-11abs ↗pdf ↗

Uncertain Bayesian Networks: Learning from Incomplete Datastat.ML

This paper improves learning uncertain Bayesian networks from incomplete data.

problem Learning conditional probabilities in Bayesian networks with limited data.

method Develops methods to estimate and quantify uncertainty in conditional probabilities with incomplete data.

result Improves state-of-the-art approaches for handling uncertain Bayesian networks with incomplete data.

Bayesian networks uncertainty incomplete data conditional probabilities learning algorithms

2022-08-08abs ↗pdf ↗

α

-Hypergeometric Uncertain Volatility Models and their Connection to 2BSDEsq-fin.PR

Proposes a new uncertain volatility model with worst-case scenario analysis.

problem Modeling and pricing options under uncertain volatility.

method Connection between G-HJB equations and 2BSDEs for option pricing.

result Derives a limit model for worst-case price scenario.

uncertain volatility 2BSDEs G-HJB equations worst-case scenario option pricing

2021-08-16abs ↗pdf ↗

Uncertain Volatility Models with Stochastic Boundsq-fin.MF

In this paper, we propose the uncertain volatility models with stochastic bounds. Like the regular uncertain volatility models, we know only that the true model lies in a family of progressively measurable and bounded processes, but instead of using two deterministic bounds, the uncertain volatility fluctuates between …

2017-02-16abs ↗pdf ↗

Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guaranteesstat.ML

UnKGCP generates prediction intervals for uncertain knowledge graphs with statistical guarantees.

problem Lack of quantified predictive uncertainty in existing UnKGE methods.

method Proposes extsc{UnKGCP} framework using conformal prediction with a novel nonconformity measure.

result Sharp prediction intervals effectively capture predictive uncertainty in diverse UnKGE methods.

Uncertain Knowledge Graphs Prediction Intervals Conformal Prediction Uncertainty Quantification Statistical Guarantees

2025-10-18abs ↗pdf ↗

Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputscs.LG

AIRBO optimizes robustly under uncertain inputs.

problem Challenges in Bayesian Optimization due to input uncertainty.

method AIRBO models arbitrary input uncertainty with MMD and accelerates posterior inference.

result AIRBO achieves state-of-the-art performance under various input uncertainties.

Bayesian Optimization Robustness Uncertain Inputs Maximum Mean Discrepancy Nystrom Approximation

2023-10-31abs ↗pdf ↗

Robust Pricing of Equity-Indexed Annuities under Uncertain Volatility and Stochastic Interest Rateq-fin.PR

Novel pricing method for equity-indexed annuities under uncertain volatility and stochastic interest rate.

problem Pricing equity-indexed annuities with early surrender risk under uncertain market conditions.

method Advanced financial modeling techniques, including uncertain volatility framework and Hull-White model for interest rate dynamics. Numerical algorithm using tree-based framework with local volatility optimization.

result High effectiveness of the proposed numerical algorithm compared to machine learning-based methods.

Equity-indexed annuities Uncertain volatility Stochastic interest rate Tree-based framework Local volatility optimization

2025-02-14abs ↗pdf ↗

Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocitystat.ML

GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.

problem Uncertainty in atrial conduction velocity calculations.

method Gaussian Process Manifold Interpolation (GPMI) on human atrial manifolds.

result GPMI accounts for atrial topology and calculates CV uncertainty.

Gaussian process manifold interpolation atrial fibrillation conduction velocity uncertainty

2020-04-22abs ↗pdf ↗

Computing Black Scholes with Uncertain Volatility-A Machine Learning Approachq-fin.MF

A machine learning approach to compute Black-Scholes prices with uncertain volatility.

problem Approximating financial markets with continuous-time models like Black-Scholes when data is discrete.

method Generalized Polynomial Chaos (gPC) method combined with a machine learning technique called Bi-Fidelity.

result Efficient numerical method to quantify uncertainty in derivative pricing.

Black-Scholes uncertain volatility machine learning Polynomial Chaos gPC

2022-02-15abs ↗pdf ↗

Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lenscs.LG

This paper rates robustness of multi-modal time-series forecasting models.

problem Robustness of AI systems in time-series forecasting is crucial for stakeholders.

method Causal analysis to assess robustness of MM-TSFM models.

result Multi-modal forecasting models are more robust than numeric models.

time-series forecasting robustness multi-modal causal analysis AI fragility

2024-06-12abs ↗pdf ↗

Mean-Variance Analysis in Bayesian Optimization under Uncertaintystat.ML

The paper tackles mean-variance analysis in Bayesian optimization under uncertainty.

problem Optimizing decisions in uncertain environments considering trade-offs between average and variance of risk.

method Developed bounds for mean and variance risk measures in Gaussian Process models and proposed AL algorithms for multi-task, multi-objective, and constrained optimization scenarios.

result Proposed AL algorithms effectively address the mean-variance trade-off in uncertain optimization scenarios.

Bayesian Optimization Mean-Variance Analysis Active Learning Gaussian Process Risk Measures

2020-09-17abs ↗pdf ↗

Online Learning with Uncertain Feedback Graphscs.LG

Novel algorithms for online learning with uncertain feedback graphs reduce regret.

problem Uncertainty in feedback graphs hinders traditional online learning approaches.

method Developed novel online learning algorithms to handle uncertain feedback graphs.

result Proved sublinear regret under mild conditions for the proposed algorithms.

online learning uncertain feedback regret minimization graph theory

2021-06-15abs ↗pdf ↗

Discriminative Feature Selection for Uncertain Graph Classificationcs.LG

Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objec…

2013-01-28abs ↗pdf ↗

Training of Neural Networks with Uncertain Data: A Mixture of Experts Approachstat.ML

uMoE trains NNs with uncertain data by embedding uncertainty into training.

problem Managing aleatoric uncertainty in NN-based predictive models.

method Divide and Conquer strategy, Expert components, Gating Unit.

result uMoE outperforms baseline methods in uncertainty management.

Neural Networks Uncertainty Mixture of Experts Aleatoric Uncertainty Divide and Conquer

2023-12-13abs ↗pdf ↗

Learning Strategies in Decentralized Matching Markets under Uncertain Preferencescs.GT

Study on learning strategies in matching markets with uncertain preferences.

problem Decision-making in scarcity of shared resources with unknown agent preferences.

method Representation of preferences in a reproducing kernel Hilbert space, learning algorithm for uncertainty.

result Optimal strategies derived to maximize agents' expected payoffs, with stability and fairness properties.

matching markets uncertain preferences reproducing kernel Hilbert space learning algorithms stability

2020-10-29abs ↗pdf ↗

Portfolio Rebalancing under Uncertainty Using Meta-heuristic Algorithmq-fin.PM

In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain paramet…

2018-12-18abs ↗pdf ↗

Asian Option Pricing under Uncertain Volatility Modelq-fin.PR

In this paper, we study the asymptotic behavior of Asian option prices in the worst case scenario under an uncertain volatility model. We give a procedure to approximate the Asian option prices with a small volatility interval. By imposing additional conditions on the boundary condition and cutting the obtained Black-S…

2018-08-02abs ↗pdf ↗

Modelo de maturidade em gerenciamento de riscos em projetos (Project Risk Management Model Maturity)q-fin.RM

The globalization feeded by the technology explosion that begans in the end of the last century, started the world to change faster every day. The only today's certain is the tomorrow's uncertain. Risk is defined as uncertain where one or many causes composed of ocurrence probality can generate an impact or consequence…

2018-01-19abs ↗pdf ↗

Counterfactually Comparing Abstaining Classifiersstat.ML

Paper introduces a method to evaluate abstaining classifiers by considering missing predictions as counterfactuals.

problem Lack of a principled approach to evaluate and compare abstaining classifiers, especially when missing predictions are important.

method Develops a novel approach to treat abstentions as missing data, defining counterfactual scores and using causal inference methods to estimate them.

result Shows that under certain conditions, counterfactual scores can be identified and estimated efficiently, improving evaluation of abstaining classifiers.

abstaining classifiers missing data counterfactuals observational causal inference evaluation methods

2023-05-17abs ↗pdf ↗