sensitivity analysis

This study addresses the ad hoc division between calibrated and estimated parameters in structural modeling, which often lacks a systematic foundation and can induce substantial bias due to calibration errors. For the first time, the partitioning problem is formalized as an optimization task, and a sensitivity-minimization criterion is proposed for selecting the optimal split. Specifically, a sensitivity statistic—constructed from local derivatives—quantifies how target estimates respond to perturbations in calibrated parameters. The method selects the partition that minimizes this statistic, thereby reducing worst-case local bias. Notably, it avoids repeated re-estimation and is applicable across a broad class of structural models. An application to a New Keynesian model demonstrates that the chosen partition significantly enhances estimation robustness and credibility under sizable calibration errors.

This paper addresses the external validity of personalized treatment policies when deploying them in target populations whose covariate and potential outcome distributions differ from those of the experimental population. To tackle joint distributional shifts in potential outcomes and covariates, we propose— for the first time—a Wasserstein distributionally robust framework for policy estimation, unifying causal inference, heterogeneous treatment effect modeling, and robust optimization. Theoretically, the method guarantees near-optimal welfare performance under broad classes of distributional shifts and substantially improves generalizability across heterogeneous populations. Our key contributions are: (1) characterizing the robustness boundary of experimentally optimal policies to shifts in the potential outcome distribution; and (2) developing a unified estimation paradigm that simultaneously handles shifts in both outcome and feature distributions. The resulting estimator is provably consistent and exhibits strong empirical robustness under realistic distributional mismatches.

In causal inference, sensitivity parameters are often difficult to calibrate due to their lack of intuitive causal interpretation, and existing methods ignore the sampling uncertainty in measured confounder estimation, leading to biased robustness assessments. This paper proposes a calibration-based sensitivity model: it directly constrains the strength of unmeasured confounding as a multiple of the estimated effect of measured confounders—endowing the sensitivity parameter with a clear causal interpretation (“unmeasured-to-measured confounding ratio”). It is the first to systematically incorporate the sampling variability of measured confounder estimates, thereby correcting inferential bias in bounding. Leveraging double robustness, nonparametric efficiency, and asymptotic normality theory, we construct three computationally tractable bounding models for the average treatment effect. Empirical analysis of maternal smoking’s effect on birth weight shows that conventional methods can substantially overstate or understate conclusion robustness. Our approach enhances the interpretability, calibration validity, and statistical reliability of sensitivity analysis.

This paper addresses the challenge of welfare analysis for dynamic models in high-dimensional state spaces. Methodologically, it proposes an estimable and inferential welfare metric framework grounded in doubly robust estimation and dynamic dual representation, enabling unbiased inference on average welfare and its marginal effects without explicit value function estimation. The approach accommodates arbitrary value function estimators—including Lasso and deep neural networks—and automatically corrects their estimation bias without imposing restrictive assumptions on bias structure. Theoretically, it establishes consistent estimation and asymptotically valid inference procedures for average welfare, average marginal welfare effects, and decomposition into direct and indirect effects under high-dimensional dynamic environments. Empirically, the method is applied to a dynamic model of teacher absenteeism, successfully estimating average teacher welfare and demonstrating strong performance, validity, and robustness in a real-world high-dimensional dynamic setting.

This paper addresses identification issues in skill formation structural models arising from standard normalization constraints, demonstrating that conventional scale and location normalization—particularly under CES utility—distorts key policy parameters, induces estimation bias, and undermines policy recommendations. Methodologically, it integrates structural identification analysis, characterization of identified sets, counterfactual inference, and statistical testing and correction of normalization constraints. The study establishes, for the first time, necessary and sufficient conditions for “true normalization,” achieving point identification of policy parameters under weaker assumptions than existing approaches; it further identifies and rectifies over-identification induced by scale constraints in CES models. A practical correction framework is proposed that preserves compatibility with standard estimators, ensures robustness of investment strategies to unit changes, and enhances the credibility of counterfactual predictions and policy evaluations.

This study addresses a critical limitation of conventional Cross-Impact Balance (CIB) analysis, which yields only static consistent scenarios and cannot quantify dynamic structural aspects such as transition efforts, key leverage points, timing of adjustments, or responses to external shocks. To overcome this, the authors introduce linear response theory into the CIB framework, exploiting the structural isomorphism between the CIB drift matrix and the Leontief input-output matrix. This enables the derivation of four analytical constructs—Type I cross-impact multipliers, perturbation budgets, impulse response functions, and unit impulse shock profiles—each admitting closed-form solutions that characterize indirect effects, transition resistance, dynamic adjustment pathways, and network sensitivity in socio-technical systems. Applied to an energy transition case, the approach successfully computes all dynamic indicators across five structural equilibria, offering a transferable quantitative toolkit for assessing system resilience, designing transition pathways, and informing policy interventions.

This study addresses the challenge of jointly modeling calibration and control parameters in computer model calibration, where the distribution of calibration parameters is unknown while that of control parameters is known. To tackle this issue, the authors propose a nonparametric Bayesian calibration method based on measure decomposition. The approach preserves the known marginal distribution of the control parameters while employing stochastic process modeling and Bayesian inference to construct a posterior distribution over the input space that aligns with field observations. Notably, this work is the first within a nonparametric calibration framework to explicitly maintain the prior distributional properties of the control parameters, thereby substantially enhancing the physical consistency and scientific credibility of the calibration results.

This study addresses a key limitation of traditional instrumental variable (IV) models, which assume deterministic relationships between treatment selection and potential outcomes under an instrument, thereby failing to capture stochastic decision-making in real-world settings. To overcome this restriction, the paper develops a micro-founded framework in which both potential outcomes and treatment choices exhibit individual-level randomness. Response types are redefined as state-dependent treatment probabilities coupled with distributions of potential outcomes, grounded in expected utility maximization subject to information constraints. Within this framework, conventional IV estimands are reinterpreted not merely as local average treatment effects for compliers, but as population-weighted averages of treatment effects, where weights correspond to individual changes in treatment probability induced by the instrument. This approach yields a more flexible and interpretable characterization of treatment effect heterogeneity.

This study addresses the limitations of traditional multifactor interest rate model calibration, which often neglects the influence of market data and parameter uncertainty, thereby hindering reliable assessment of calibration quality. The authors formulate calibration within a nonlinear regression framework, demonstrating that minimizing the root mean squared relative error (RMSRE) is equivalent to weighted least squares. They introduce, for the first time, an influence diagnostic framework tailored to stochastic interest rate models, enabling local sensitivity analysis with boundary constraints and confidence interval estimation. Their approach integrates weighted hat matrices, influence functions, the functional delta method, and an efficient Jacobian decomposition leveraging analytical gradients from at-the-money (ATM) cap prices. Empirical analysis using euro ATM cap data from 2016–2025 reveals pronounced leverage effects near parameter boundaries, effective dimensionality reduction, and a marked shift in parameter stability after 2022, indicating that low RMSRE alone does not guarantee trustworthy calibration.

This study addresses the lack of a systematic framework for identifying critical input variables and conducting sensitivity analysis under uncertainty in complex simulations, particularly in military decision-making contexts. The authors propose a unified sensitivity analysis framework that integrates local and global methods—including variance-based, derivative-based, screening, and uncertainty quantification techniques—and strategically maps these approaches to specific decision objectives such as factor prioritization, fixing, variance reduction, and mapping. Innovatively, the framework introduces a “sensitivity audit” mechanism to enhance traceability of model assumptions and promote responsible model usage. By providing a structured guide for high-dimensional, complex simulation systems, this work significantly improves model interpretability, transparency, and the credibility of decisions derived from such models.