Distributionally robust risk evaluation with a causality constraint and structural information
This work studies the distributionally robust evaluation of expected values over temporal data. A set of alternative measures is characterized by the causal optimal transport. We prove the strong duality and recast the causality constraint as minimization over an infinite-dimensional test function space. We approximate test functions by neural networks and prove the sample complexity with Rademacher complexity. An example is given to validate the feasibility of technical assumptions. Moreover, when structural information is available to further restrict the ambiguity set, we prove the dual formulation and provide efficient optimization methods. Our framework outperforms the classic counterparts in the distributionally robust portfolio selection problem. The connection with the naive strategy is also investigated numerically.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Causality-oriented robustness: exploiting general noise interventions
Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, such as empirical risk minimization or dist…
Causal InferenceDomain AdaptationPredictionSemi-supervised Domain AdaptationDistributionally Robust Model Predictive Control with Total Variation Distance
This paper studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets. For a discrete-time linear system with additive disturbances, we provide a condition…
Computational EfficiencyModel Predictive ControlDomain Adaptative Causality Encoder
Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data. Therefore, the resulting models perform poorly w…
Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairness
In recent years, Wasserstein Distributionally Robust Optimization (DRO) has garnered substantial interest for its efficacy in data-driven decision-making under distributional uncertainty. However, limited research has ex…
Conditional Risk Minimization with Side Information: A Tractable, Universal Optimal Transport Framework
Conditional risk minimization arises in high-stakes decisions where risk must be assessed in light of side information, such as stressed economic conditions, specific customer profiles, or other contextual covariates. Co…
Portfolio Optimization