Probability & Statistics · Mathematics & Statistics
Methodology
The newest papers in Methodology that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
353 papers, from 4 September 2026 to 5 November 2026: 353 arXiv preprints.
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Newest papers, page 5
Practical Advantages of Online Conformal Prediction for Data Streams
Full conformal prediction offers finite-sample marginal coverage guarantees under exchangeability, but exact computation can be costly, while its classical guarantees do not directly extend to…Optimal aggregation in multiclass classification
This paper studies the problem of aggregation in multiclass classification, generalizing existing results for binary classification.Backtracking Direct, Indirect and Total Effects
Natural direct and indirect effects are defined through cross-world interventional counterfactuals.Federated Community Detection in Bipartite Networks
We study federated spectral estimation when a large matrix is horizontally partitioned across servers that cannot share raw data.Mediation and moderation in distance-based regression with triangular fuzzy data: simulation evidence and an application to academic performance
We study mediation and moderation in the distance-based regression model for triangular fuzzy responses, in which the center and transformed spreads of the response are linear in the components of…Causal Robustness from Synthetic Environments
We study robust prediction when the training data come from a single observational distribution.R2-GP: Variance-Calibrated Priors for Additive Gaussian Processes
We develop R2-GP, an R2D2-type prior for Gaussian process (GP) generalised additive models (GAMs) that calibrates explained variance ($R^2$) for Matérn covariance kernels.Dirichlet-Tree Allocation Priors for Bayesian Causal Forests
Bayesian Causal Forests (BCF) estimate heterogeneous treatment effects with a separate ensemble of treatment trees.High-dimensional covariate-assisted ranking
Ranking data in modern applications often involves covariate vectors whose dimension grows with the number of objects.Modular Bayesian Inference for Partially Misspecified Mixtures with Uncertain Class Labels
Scientific inference often involves observations from multiple latent populations whose class memberships are uncertain and must be inferred from available data.A plug-in estimator of the non-iterative conditional expectation g-formula using long short-term memory models
The non-iterative conditional expectation (NICE) plug-in estimator of the g-formula can be used to estimate the causal effects of sustained treatment strategies, even in the presence of…Estimating Causal Bounds from Observational Data With Neural Generative Models
Identifying causal bounds from discrete observational data is a fundamental problem in causal inference.DR-Learners for Generalizing CATEs from a Randomized Controlled Trial
Randomized clinical trials are essential for evaluating interventions, yet average treatment effects can obscure treatment effect heterogeneity, making conditional average treatment effects (CATEs)…Conditional Weight of Evidence for Gradient-Boosted Credit Models: Estimation, Additivity and Its Relation to Adverse Action Reasons
Traditional weight of evidence assigns a credit characteristic a value based on its association with default across a population.Martingale Posterior Cox Processes
Cox processes provide a principled Bayesian nonparametric framework for modeling event-based data through an underlying intensity function.Conformal Policy Selection: When to Deploy AI Decisions for Targeted Intervention
Artificial-intelligence (AI) policies are increasingly used to make consequential decisions in medicine, finance, education, and public services, yet deploying them responsibly requires knowing not…Causal Discovery beyond Conditional Independence: Identifiability, Algorithms, and Statistical Inference for Directed Acyclic and Cyclic Graphs
Learning causal structure from data requires assumptions about mechanisms, noises, observation schemes, and interventions.A general recipe for data-adaptive anytime-valid envelopes for iterative algorithms
Most convergence guarantees for iterative stochastic algorithms are stated at a fixed iteration count and do not justify stopping at a data-dependent time.Graph-Regularized Synthetic Control for Stable Counterfactual Estimation under Donor Redundancy
Synthetic control methods estimate counterfactual outcomes by weighting untreated donor units to match a treated unit before treatment.lepidoptera: A Domain-Agnostic Engine for Causal Simulation with Mixed Variable Types, Vine Copula Dependence, and Individual-Level Counterfactual Propagation
lepidoptera is an open-source R and Python package for simulation from structural causal models with heterogeneous variable types.