verify model assumptions

This paper addresses the lack of robust design foundations for sensitivity analysis in finite-population causal inference. Methodologically, it introduces a novel sensitivity analysis framework grounded in the experimental design distribution—first integrating design-based distributions with partial identification theory to construct model-free, non-asymptotic confidence intervals for the average treatment effect (ATE). It further reinterprets the role of randomization in sensitivity analysis and provides a new design-driven rationale for covariate balance checks. Key contributions include: (1) model-free, finite-population inference under heterogeneous treatment effects; (2) robust ATE confidence intervals with clear identification-theoretic interpretation; and (3) empirical validation across three real-world applications, demonstrating reliability and practicality in small-sample and highly heterogeneous settings.

This study addresses the common practice in traditional causal inference of treating diagnostic checks—such as covariate balance, pre-trends tests, and instrumental variable validity—as external to the estimation procedure, which can introduce inferential bias through selective reporting. The paper innovatively endogenizes diagnostic statistics into the estimation process by residualizing (i.e., orthogonalizing) baseline estimators with respect to these diagnostics within a linear adjustment framework. This approach delivers three key advantages: it eliminates bias from selective reporting, reduces variance under correctly specified models, and minimizes worst-case bias under local misspecification. Empirical application to the randomized trial of Kaur et al. (2024) demonstrates that, even when all balance tests are satisfied, the method substantially improves point estimate precision and narrows standard errors—equivalent to a roughly 10% increase in effective sample size.

论文提出Evidence-Calibration-Stability框架，通过区分证据、校准和稳定性来解决模型不确定性下的假设检验问题。

This paper addresses the low statistical power and poor resolution of conventional placebo tests in synthetic control method (SCM) causal inference under small-sample settings—particularly when α = 0.05 and the number of donor units *N* is small. We propose a leave-two-out randomization inference framework that rigorously controls Type I error rates in finite samples while substantially improving test resolution and statistical power, even under stringent significance levels (α < 1/*N*). Unlike permutation or rank-based tests, our framework accommodates non-uniform treatment assignment and integrates formal sensitivity analysis for robust causal inference. Empirical results demonstrate that, under moderate effect sizes, the proposed method achieves lower actual Type I error rates and higher statistical power compared to standard approaches.

This paper addresses the validity of negative control variable (NCV) falsification tests in instrumental variable (IV) design. We identify a critical flaw: conventional NCV tests implicitly impose untestable functional-form restrictions—beyond standard exclusion and independence assumptions—leading to false rejection of valid IVs. To resolve this, we develop a unified theoretical framework that formally characterizes the identification conditions required for NCVs and establishes principled variable selection criteria. We further uncover and correct the implicit assumption bias inherent in existing NCV tests, proposing robust implementation guidelines. Methodologically, our approach integrates causal inference, IV identification theory, and conditional independence testing. The resulting framework substantially enhances the robustness, interpretability, and empirical reliability of IV analyses.

This study addresses the problem of verifying whether a given observational formula correctly identifies a target interventional distribution in causal graphical models, going beyond mere identifiability assessment. To this end, it introduces a falsification-driven verification framework that decouples verification from identification for the first time: an efficient falsifier first eliminates incorrect formulas, and a verifier—provably almost surely correct under regular exponential family models—is then constructed atop this filter. As an application, the authors develop a “gateway test” that enumerates all valid variable sets satisfying the front-door criterion, with theoretical guarantees on verification reliability. This approach substantially enhances both the practicality and rigor of validating interventional distributions.

This study addresses a critical gap in current mechanistic interpretability research, where causal claims are often made without explicit articulation of identification assumptions, conflating validation metrics with genuine causal identification. The authors conduct the first systematic audit of 40 representative papers, employing dual-coder annotation and qualitative content analysis to evaluate the alignment between stated causal claims and underlying identification assumptions. Findings reveal that the vast majority of papers omit dedicated statements of identification assumptions and routinely substitute validation for identification. To rectify this, the paper proposes a standardized disclosure framework encompassing explicit assumption statements, clear naming of identification strategies, and sensitivity analyses, thereby advocating for more rigorous standards of causal inference in the field.

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.

This study addresses the challenge of balancing false positive and false negative risks in AI fairness auditing under a specified tolerance threshold. To this end, it constructs a unified statistical testing framework tailored to two distinct objectives: violation certification and sensitivity screening. The proposed methodology introduces a constrained empirical likelihood test alongside an adaptive boundary surrogate principle. By integrating least favorable point calibration, split empirical likelihood, and spurious mark rate control strategies, the framework achieves differentiated trade-offs between error control and detection sensitivity. Numerical experiments validate the method’s capacity for flexible risk balancing, while an empirical analysis on the COMPAS dataset demonstrates its practical effectiveness in predictive fairness auditing.

This work proposes a novel representation learning framework that addresses the limited representational capacity of existing methods in complex scenes by integrating adaptive multi-scale fusion with contrastive learning. The approach dynamically aggregates multi-level features and incorporates a structure-aware contrastive loss, thereby enhancing the model’s ability to jointly capture fine-grained semantics and global contextual information. Extensive experiments demonstrate that the proposed framework consistently outperforms state-of-the-art methods across multiple benchmark datasets, achieving substantial improvements in both accuracy and robustness. These results establish a promising new direction for unsupervised and semi-supervised representation learning.