comparative statics analysis

This paper identifies a fundamental bias in estimating average treatment effects (ATE) in continuous-parameter A/B tests—particularly price experiments—arising from interference among market participants. In pricing contexts, conventional estimators of profit change expectations can exhibit sign reversal, leading firms to adopt profit-damaging pricing policies. To address this, we propose a lightweight debiasing method requiring only equal partitioning of experimental units. We are the first to systematically characterize the “sign reversal” phenomenon and prove its ubiquity in two-sided markets and multi-category commission pricing. Through structural modeling and differential analysis, we derive an explicit closed-form expression for the bias and theoretically demonstrate that the classical estimator can indeed flip sign. Empirical evaluations across diverse market settings confirm that our method consistently restores correct decision directionality, thereby ensuring reliable causal inference.

Standard difference-in-differences (DID) methods struggle to identify counterfactual distributions under regulatory policies—such as minimum wage laws—when confronted with mass points, distributional discontinuities, nonstationarity, or unobserved selection bias. This paper proposes a unified partial identification framework grounded in a copula stability assumption, applicable to discrete, continuous, and mixed outcome variables. Under continuity and monotonicity, the framework collapses to the point-identification result of Athey & Imbens (2006), and it is transformation-invariant. Integrating DID, copula modeling, and partial identification theory, the approach yields sharp bounds on the counterfactual distribution. Empirically, it precisely quantifies the causal impact of minimum wage increases on the joint distribution of employment and earnings. The resulting bounds are highly informative, substantially extending both the applicability and robustness of policy evaluation methods in settings where conventional DID assumptions fail.

This paper addresses causal effect estimation under continuous, time-varying treatments (e.g., taxes, tariffs, prices), extending the canonical difference-in-differences (DID) framework. Methodologically, it introduces a longitudinal comparison identification strategy anchored at baseline treatment levels to identify a weighted average of the treatment effect slope; constructs a doubly robust, √n-consistent, and asymptotically normal nonparametric estimator; and rigorously generalizes DID to settings with continuous treatment in every period—including an extension to instrumental variable settings. The approach avoids strong parametric assumptions on the treatment function and preserves testability of the parallel trends assumption. Empirically, the method successfully estimates the price elasticity of gasoline demand, demonstrating its validity and robustness in real-world economic policy evaluation.

This paper addresses the inefficiency and fragility of conventional stated-preference experiments for probabilistic choices, where ex ante expected returns and willingness-to-pay (WTP) estimates rely on multiple choice rounds and strong parametric assumptions—leading to lengthy surveys and low feasibility for ex ante policy evaluation. We propose a nonparametric identification method requiring at most two probabilistic choices per respondent. It imposes no functional-form assumptions on utility and, for the first time, fully identifies both the population distribution of ex ante expected returns and WTP for structured preference objects (e.g., multidimensional job attributes). Theoretical foundations integrate nonparametric identification theory with structured discrete choice modeling. Applied to elite student employment preferences in Côte d’Ivoire, the method robustly identifies a significant upward effect of public-sector jobs on private-sector hiring costs—demonstrating both empirical validity and direct policy relevance.

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 key limitation of traditional regression discontinuity and kink designs, which focus solely on average treatment effects while ignoring how policies reshape the entire outcome distribution. The authors propose a novel distributional framework that introduces the Wasserstein distance into causal inference to quantify discrepancies between conditional distributions at the treatment threshold. By leveraging an L-moments-based orthogonal decomposition, the method disentangles changes in distributional features—such as location, scale, and skewness—to uncover sources of treatment effect heterogeneity. In the fuzzy kink setting, this approach yields new identification results. Empirical applications to two real-world natural experiments demonstrate that distributional effects can differ markedly from conventional average effects, underscoring the method’s explanatory power and practical relevance.

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 challenge of unobserved confounding in experiments with spillover effects by optimizing both treatment assignment and estimation to minimize worst-case asymptotic variance. It characterizes the optimal treatment assignment distribution and integrates it with exposure mapping to maximize spillover signal strength while controlling its variability. The authors further develop a recentered instrumental variable estimator that efficiently leverages spillover-induced variation. Building on this, they derive experimental design principles that jointly account for signal and noise, along with computationally tractable approximation schemes applicable to clustered exposures and general networks—including bipartite graphs. In a semi-synthetic experiment drawn from development economics, the proposed approach substantially reduces standard errors, yielding effective sample size gains of 50% to over 100%.

This study addresses the unresolved trade-off between investing in predictive capabilities and alternative policy instruments—such as capacity expansion or service quality improvements—in resource-scarce allocation settings. The authors propose an empirical framework integrating causal inference, counterfactual simulation, and welfare economics, and introduce rvp, the first operational open-source toolkit for quantifying the marginal welfare effects of prediction in resource allocation and enabling cross-context policy comparisons. The framework’s validity is demonstrated through two empirical applications: job placement services in Germany and poverty targeting in Ethiopia. Results reveal that the welfare value of prediction is highly context-dependent, offering policymakers a scalable benchmark for evaluating and prioritizing interventions under constrained resources.

This study addresses the joint optimization of personnel assignment and performance evaluation rules in networked organizations where employee output depends on both individual effort and exogenous, uncontrollable advantages. The authors develop a dual-network framework: a competition network that shapes effort decisions and a spillover network that propagates advantage effects. By integrating game-theoretic equilibrium analysis, Katz–Bonacich centrality, and mechanism design theory, they demonstrate that optimal evaluation rules generally deviate from true output—employing negative matching with low weights when effort dominates, and positive matching with high weights when advantages dominate. The work also provides the first quantification of efficiency loss due to pairwise stability constraints and characterizes the intrinsic relationship between equilibrium effort, network position, and effective advantage, offering tailored optimal strategies for diverse production environments.