construct validity assessment

Existing research frequently suffers from model misspecification of formative constructs, and the absence of a consensus-based validation methodology leads scholars to erroneously apply reflective measurement frameworks, thereby compromising construct validity. Method: This paper introduces the first dedicated, multi-stage validation framework for formative constructs, integrating systematic literature review, descriptive statistics, multicollinearity diagnostics, and formative-model-specific tests to rigorously distinguish formative (causal) from reflective (effect) measurement logic. Contribution/Results: The framework ensures both theoretical rigor and practical feasibility, substantially enhancing the psychometric soundness and statistical integrity of formative indicators. It provides a reproducible, defensible methodological pathway for scale development and construct validation, directly addressing longstanding measurement challenges in behavioral and social science research.

This study addresses the uncontrolled false positive rates and misleading inferences arising from commonly used simulation methods in shift-share designs. We systematically evaluate prevailing inferential approaches in empirical research through a suite of multilevel simulation experiments. By comparing Monte Carlo analysis with counterfactual data-generating mechanisms, we uncover non-monotonic trade-offs among fidelity, sensitivity, and risk of misdirection across simulation designs. We propose a novel “progressive-fidelity simulation framework,” demonstrating that low-fidelity simulations suffice to expose fundamental inferential flaws, whereas high-fidelity simulations detect subtle, previously overlooked biases—substantially improving detection power. The framework balances interpretability and computational efficiency, offering a reproducible and scalable paradigm for assessing the robustness of causal inference methods.

Current machine learning evaluation practices predominantly rely on surface-level performance metrics, often neglecting the internal mechanisms of models. This work proposes trustworthy interpretability as a central evaluation paradigm and, for the first time, systematically demonstrates that it satisfies core criteria from the philosophy of science—namely falsifiability, reproducibility, and predictive power. By constructing an evaluation framework that integrates causal analysis with mechanistic probing, the study delineates three functional pathways through which interpretability enables the identification of behavioral origins, detection of latent flaws, and prediction of potential failure modes. This approach advances model assessment beyond performance-oriented benchmarks toward a deeper understanding of underlying mechanisms.

Data uncertainties—such as measurement errors, missing values, and erroneous links—undermine the credibility of policy decisions. Method: This paper proposes a decision-stability-oriented sensitivity analysis framework that shifts the analytical focus from parameter deviation to decision robustness. It introduces an interpretable, decision-level sensitivity metric and integrates counterfactual modeling, hypothesis-driven perturbation sampling, decision boundary tracking, and interactive visualization. Contribution/Results: Evaluated on two real-world policy domains—U.S. presidential vote prediction and childhood lead exposure assessment—the framework significantly enhances policymakers’ awareness of analytical robustness, explicitly delineates credible decision intervals, and provides an actionable confidence assessment tool for data-informed policymaking under data imperfections.

Machine learning benchmarks commonly assume that empirical scores directly support scientific claims—e.g., about image classification capability or policy-effect prediction—yet such inferences rely on unstated theoretical assumptions. Method: This paper introduces, for the first time, the psychometric framework of construct validity to ML benchmarking, systematically formalizing the theoretical conditions under which benchmark scores can substantiate distinct levels of scientific claims: engineering improvement, causal inference, and human behavioral modeling. Contribution/Results: Through philosophical analysis and cross-validated empirical case studies—ImageNet (computer vision), WeatherBench (scientific forecasting), and the Fragile Families Challenge (social science)—the framework exposes latent assumptions underlying performance rankings. It thereby enhances the epistemic rigor and explanatory power of ML benchmarks, enabling more principled interpretation of scores as evidence for domain-specific theoretical assertions.

This study addresses a critical yet previously unrecognized issue in observational causal inference: measurement-induced confounding, wherein latent variables—such as motivation or self-efficacy—are imperfectly measured, leading to biased estimates of adjusted causal effects. The authors formally identify and name this problem, moving beyond conventional two-stage adjustment approaches. They propose a novel Bayesian joint estimation framework that simultaneously models the latent variable’s measurement structure, the treatment assignment mechanism, and the potential outcomes model. This integrated approach effectively corrects bias in average treatment effect estimation and restores the nominal coverage of uncertainty intervals, thereby substantially enhancing the reliability of causal inferences drawn from observational data with error-prone proxies for unobserved confounders.

This study addresses the risk of misallocating capital and authority when inferring individual skill from outcome records in high-noise, low-effective-sample decision contexts. The authors propose a two-dimensional evaluation framework based on outcome noise intensity and the number of effective independent observations, revealing that prevalent performance assessments—such as those for mutual funds, venture capital, and executive evaluations—typically fall within regions where skill attribution is statistically unreliable. To mitigate this, they adapt causal validation logic from medical research, integrating statistical inference with effective sample size estimation to quantify how noise distorts skill assessment. When signal strength is insufficient, the paper advocates replacing individual-level attribution with group-level empirical methods, offering a more robust approach to evaluation in high-stakes decision domains.

Current AI benchmarks lack guarantees regarding the overall validity of reasoning chains when extrapolating from limited evidence to real-world deployment. This work introduces the concept of “projectibility,” emphasizing the coherent transmission of premises, assumptions, and uncertainties across reasoning steps, and articulates a “non-compositional principle” demonstrating that locally valid inferences may collectively fail. Drawing on philosophical epistemology—particularly the problem of projectibility—and integrating frameworks of argumentative validity with reanalysis of case studies and simulation experiments, the authors develop a projectibility auditing methodology. Applying this approach to legal AI reveals a critical disconnect: while benchmark evaluations and deployment studies may each appear sound in isolation, they often fail to align in practice. Simulations further show that aggregate stability can obscure underlying discrepancies, thereby validating the proposed auditing framework.

Current performance evaluation metrics—such as accuracy and F1 score—are typically reported as point estimates, ignoring the uncertainty induced by data clustering structures. This oversight often leads to underestimation of variability and potentially misleading model comparisons. To address this, this work proposes a unified framework that expresses a broad class of performance metrics as smooth functionals of the confusion matrix probabilities. By integrating a cluster-robust sandwich variance estimator, the framework enables valid confidence interval construction, hypothesis testing, and paired model comparison. It represents the first systematic application of cluster-robust inference to predictive performance evaluation, accommodating both binary and multiclass settings, and further provides asymptotic theory–based methods for power and sample size calculations. Simulations demonstrate that the proposed approach achieves near-nominal coverage across diverse dependence structures and substantially outperforms conventional methods that ignore clustering; real-data analyses confirm that accounting for clustering can materially alter evaluation conclusions.

Estimating the functional relationship between a continuous exposure and a binary outcome is challenging when covariates are measured with error. This study presents the first systematic evaluation of Simulation-Extrapolation, Regression Calibration, multiple imputation, and Bayesian correction methods, each coupled with flexible modeling techniques—including B-splines, P-splines, and fractional polynomials—within a multi-team, fully blinded, neutral simulation framework. By generating 155 distinct simulation scenarios and repeated samples, the research quantifies the bias and variance of each approach, revealing their relative strengths and limitations. The findings not only inform method selection under measurement error but also demonstrate the feasibility and value of this neutral comparative paradigm for rigorous methodological assessment.