causal overcharge estimation

This work addresses the challenge of evaluating long-term causal effects of online advertising mechanism changes—such as reserve price adjustments—which not only affect immediate revenue but also alter user behavior, advertiser bidding, and retention dynamics, thereby violating the i.i.d. assumption underlying conventional causal inference. To overcome this limitation, the study introduces, for the first time, a stopped random walk model combined with a budget-split experimental design. Leveraging Anscombe’s theorem, Wald-type equations, and the central limit theorem, it constructs asymptotically unbiased confidence intervals for long-term treatment effects. This approach explicitly accounts for the dynamic interplay among user retention, advertiser budgets, and mechanism parameters, providing a robust causal evaluation framework that transcends the i.i.d. constraint and enables reliable assessment of long-term impacts from advertising policy changes.

This paper bridges the theoretical gap between structural demand estimation and causal inference by clarifying how nonparametric structural assumptions in traditional demand models—such as those in Berry & Haile (2014, 2024)—can be rigorously formalized as counterfactual constraints within the Neyman–Rubin potential outcomes framework. Method: It systematically recasts key identification assumptions—specifically, those underlying market-level and demographic-segment share models—as testable counterfactual independence and homogeneity conditions within the potential outcomes model, and demonstrates that cross-market counterfactual homogeneity is necessary for identifying market-level counterfactual outcomes. Contribution: The paper establishes a precise, one-to-one correspondence between structural demand assumptions and causal identification conditions, resolves conceptual ambiguities arising from disciplinary differences in notation and terminology, and provides a unified theoretical foundation for integrating methods across structural and causal paradigms.

This paper addresses point estimation and uncertainty quantification for treatment effect paths—such as dynamic effects and event-study designs—in policy evaluation. To overcome the looseness of conventional uniform confidence bands, which ignore correlations among path estimators, we propose two data-driven feasible bound methods. Our novel framework jointly enforces average-effect coverage guarantees and path smoothness constraints, integrating post-selection inference, smooth regularization, Monte Carlo simulation, and robust point estimation. The resulting confidence bands are substantially narrower while maintaining valid coverage, especially under high estimator correlation; our point estimator also demonstrates superior performance across diverse simulation settings. The key contribution is the first systematic incorporation of smoothness priors into path inference, thereby unifying statistical rigor with economic interpretability.

This paper addresses key challenges in synthetic control method (SCM) estimation of average causal effects: difficulty in bias correction, non-robust variance estimation, and sensitivity of conventional inference to model misspecification. We propose a debiased estimation framework based on K-fold cross-fitting and construct a self-normalized t-statistic that avoids explicit estimation of long-run variance. Grounded in asymptotic pivotal distribution theory, our approach is theoretically robust to model misspecification and accommodates both stationary and non-stationary data. Simulation studies and an empirical application to carbon tax-induced emissions reductions demonstrate that our method achieves substantially higher small-sample inference accuracy and statistical power compared to existing SCM-based inferential procedures. To the best of our knowledge, this is the first robust hypothesis testing framework within the SCM paradigm that obviates the need for long-run variance estimation.

Identifying counterfactual joint distributions (i.e., couplings) is essential for personalized decision-making and risk assessment, yet existing approaches—bijective structural causal models (SCMs) and optimal transport (OT)—suffer from sensitivity to noise misspecification and inability to identify higher-order couplings, respectively. This paper introduces a novel framework grounded in *cocycles*—a concept from dynamical systems theory newly imported into causal inference—to characterize invariant structures via local symmetries under intervention-induced transformations. Our approach enables nonparametric, model-free identification of counterfactual distributions without relying on parametric or semiparametric assumptions. Crucially, it is inherently robust to latent variable misspecification. We develop an efficient semiparametric cocycle estimator, demonstrating both robustness and state-of-the-art performance in simulations. Applied to 401(k) policy evaluation, our method accurately quantifies the causal effect of pension eligibility on household asset accumulation.

This study addresses the causal inference challenges in credit loss forecasting for regulatory stress testing by proposing a causal panel prediction framework that explicitly separates components learnable from data from those reliant on untestable confounding assumptions. The approach integrates iterative regression, path-conditioned mean identification, causal set identification under bounded confounding, and recursive error analysis with importance-weighted conformal calibration to decompose predictive uncertainty into three interpretable layers. It innovatively disentangles estimation uncertainty from confounding uncertainty, yielding actionable outputs including robustness metrics, extrapolation cost diagnostics, an automatic abstention mechanism, and time-domain reliability bounds. The framework’s effectiveness and practical utility are validated through simulations and semi-synthetic experiments based on real unemployment data, including retrospective analyses of extreme scenarios such as the COVID-19 pandemic.

This study addresses the challenge that digital twins in feedback systems often fail to accurately predict post-intervention equilibrium counterfactual responses under mechanism shifts. To overcome this limitation, the authors propose a verifiable and transferable causal digital twin framework grounded in causal graphical models and equilibrium selection mechanisms. By introducing cyclic selection graphs, hybrid modeling strategies, and identifiability boundaries, they demonstrate that matching only means and covariances is insufficient to ensure distribution-level counterfactual consistency, thereby establishing the necessity of structural assumptions. Leveraging linear system identification theory and statistical testing, the work derives intervention conditions dependent on mechanism changes and observational structure in synthetic feedback systems, characterizes the range of query values when point identification fails, and validates the theoretical claims empirically.

This work addresses the lack of a systematic approach to composing and ordering do-calculus rules, which hinders efficient exploration of the space of equivalent interventional queries. The paper introduces, for the first time, a derivation graph structure that formally captures the application and composition logic of do-calculus rules, systematically representing equivalence relations between observational and interventional probabilities under the do-calculus framework. Building upon this representation, the authors devise a streamlined identification procedure requiring at most four simplification steps. This approach not only reveals the intrinsic organizational structure underlying do-calculus reasoning but also enables the generation of multiple equivalent estimands for the same causal quantity, substantially improving estimation efficiency and facilitating practical applications of do-calculus.