177 papers
This paper proposes a signed block -model that simultaneously captures strong and weak balance in heterogeneous signed networks, establishing its identifiability, developing an efficient optimization algorithm, and proving asymptotic consistency for both probability estimation and community detection.
This paper relaxes the strong assumption of no unmeasured confounding between infection risk and vaccine belief in blinded randomized trials to derive nonparametric causal bounds for vaccine efficacy using linear programming and monotonicity-based methods, demonstrating their application through synthetic and semi-synthetic data.
This paper proposes the Pairwise Last Observation Time (PLOT) estimand, a novel, assumption-free causal inference method that robustly evaluates treatment effects in longitudinal studies by comparing matched individuals at their last common observation time before intercurrent events, thereby avoiding the unverifiable assumptions and sensitivity issues inherent in existing frameworks.
This paper proposes additive tree models for two-sample density ratio estimation using a novel balancing loss that enables efficient training via supervised learning algorithms and generalized Bayesian inference for uncertainty quantification, with demonstrated effectiveness in high-dimensional settings and generative model assessment.
This paper proposes a systematic framework for empirically developing evidence-based tailoring variables for adaptive interventions, arguing that while secondary data can be used, specifically designed optimization experiments (such as SMARTs or factorial designs) provide the most direct causal evidence for determining optimal measurement times, decision points, and cutoffs.
This paper proposes a causal framework for meta-analysis that introduces novel aggregation formulas for nonlinear effect measures to address the limitations of conventional methods, revealing that standard approaches can sometimes misleadingly suggest treatment benefits where causal effects are actually harmful.
This paper introduces NETCROP, a computationally efficient and accurate cross-validation method for large networks that utilizes overlapping subnetwork partitions to facilitate model selection and parameter tuning.
This paper advances the theory of small area prediction intervals by analytically demonstrating that the coverage error of empirical best linear predictors depends critically on the existence of a pivot, revealing that standard parametric bootstrap methods fail to achieve optimal accuracy without it and proposing a double parametric bootstrap approach to correct this deficiency.
This paper establishes the theoretical foundations of Vecchia approximations for Matérn Gaussian Processes by characterizing their conditional distributions via polynomial interpolation, deriving key probabilistic properties, and proving that the resulting posterior achieves optimal minimax convergence rates in nonparametric regression.
This paper proposes and evaluates three multi-fidelity parameter estimation methods—joint maximum likelihood, moment estimation, and marginal maximum likelihood—to efficiently fit parametric models to high-fidelity data by leveraging abundant low-fidelity data, with a specific focus on extreme value analysis and applications to ship motion extremes.
This paper proposes two robust criteria and a corresponding sampling-based difference-of-convex algorithm for learning optimal treatment rules that maximize truncated mean survival time and buffered survival probabilities in the presence of censored survival data, demonstrating superior performance through simulations and an AIDS clinical trial application.
This paper proposes and evaluates two nonparametric methods for estimating state entry time distributions and occupation probabilities in progressive multistate models with current status data, addressing the challenges of interval censoring through fractional at-risk sets and marginal probability ratios, and demonstrating their effectiveness via simulations and a breast cancer case study.
This paper establishes asymptotic properties of Bayesian cut distributions, including a Bernstein-von Mises theorem and Laplace approximation, and proposes a Posterior Bootstrap algorithm to achieve nominal frequentist coverage in the presence of model misspecification.
This paper extends Causal Normal Form Games to sequential settings by introducing Sequential Causal Multi-Agent Systems, but its comprehensive theoretical and empirical analysis reveals that, under standard rational assumptions, these causal frameworks offer no welfare advantage over classical Stackelberg equilibrium, thereby highlighting a fundamental incompatibility between rational choice and causal reasoning benefits in current game-theoretic models.
This paper introduces Causal Concept Graphs (CCG), a framework that combines task-conditioned sparse autoencoders with differentiable structure learning to map causal dependencies between interpretable latent features in LLMs, demonstrating through the Causal Fidelity Score that graph-guided interventions significantly enhance stepwise reasoning performance compared to existing tracing and random baselines.
This paper proposes the Time-series Neural Causal Model VAE (TNCM-VAE), a novel framework that integrates variational autoencoders with structural causal models to generate synthetic financial time series that preserve both temporal dependencies and causal relationships, thereby enabling robust counterfactual analysis and risk assessment.
This paper introduces Augmented Time Series Causal Models (ATSCM), a novel framework that integrates neural causal discovery with counterfactual reasoning to dynamically model complex, time-varying causal relationships in energy markets and enable interpretable scenario analysis for electricity price formation.
This paper proposes a novel summary-statistics-based joint analysis method that controls the joint local false discovery rate (Jlfdr), demonstrating through simulations and empirical data that it offers superior power over traditional meta-analysis methods, particularly when analyzing heterogeneous genome-wide association study datasets.
This paper proposes two probabilistic measures, Reproducibility Rate (RR) and False Irreproducibility Rate (FIR), to quantitatively evaluate the behavior of primary positive associations in replication studies, offering tools to guide study design and identify potentially true associations among irreproducible findings.
This paper proposes a Bayesian Synthetic Likelihood (BSL) framework integrated with Hamiltonian Monte Carlo to enhance Multilevel Network Meta-Regression by leveraging available subgroup-level summary statistics to impute missing individual-level covariates, thereby improving population-adjusted treatment comparisons in the presence of incomplete data.