12 papers
While acknowledging the ethical motivation behind a proposed "Hippocratic" utility function that prioritizes saving lives by avoiding harmful treatments over saving them through intervention, the author argues that the practical scope of this decision criterion is limited, as demonstrated by a counterexample.
This paper proposes a unified anomaly detection framework that identifies outliers as observations with unusually low model probability by converting them into univariate surprisal scores and estimating their tail probabilities using either empirical or extreme-value methods, demonstrating robustness against model misspecification in complex datasets.
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 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 argues for and presents an implementation of a formal representation of data analysis that externalizes an analyst's logical reasoning and assumptions, thereby enabling evaluation, visualization, and sensitivity testing of conclusions without relying solely on executable code or raw data.
This paper evaluates the effectiveness of tree-based methods, particularly stability-selected random effects trees, as robust complementary tools for detecting interaction effects in linear mixed model meta-regression, demonstrating their superiority over traditional linear methods when interactions are nonlinear and their growing competitiveness as the number of studies increases.
This paper proposes a hierarchical Bayesian dynamic game framework for competitive inventory and pricing under incomplete information, integrating Bayesian learning, strategic belief updating, and a credible-risk criterion to derive a conservative equilibrium that effectively balances profit maximization with uncertainty management.
This paper introduces a Bayesian framework for linear programming under learned uncertainty that integrates data-driven posterior updates with robust optimization and scenario-based strategies to provide rigorous, finite-sample feasibility guarantees and improved decision safety compared to naive approaches.
Although providing students with a choice of real-data contexts for homework did not significantly improve grades in an introductory statistics course, it substantially enhanced their engagement, motivation, and sense of autonomy, leading the authors to recommend incorporating varied, student-relevant contexts as a key pedagogical tool.
This paper proposes a decision-theoretic interpretation of confidence intervals as optimal probabilistic forecasts for coverage, demonstrating that the nominal confidence level is the unique optimal constant prediction and that design-based refinements can improve predictive performance without relying on Bayesian priors.
This paper challenges the strict behaviorist interpretation that a realized confidence interval either covers the parameter or it does not, arguing instead that a model-based view treating coverage as a predictive probability is mathematically consistent with long-run error rates and offers a more nuanced framework for post-data inference.