Papers

Can altruism serve as the behavioral foundation for efficient institutional design? We study full implementation in Berge equilibrium, the solution concept that formalizes strategic altruism: each agent's opponents collectively maximize her payoff. While Berge equilibrium resolves the Prisoner's Dilemma and can eliminate cooperation failures in social dilemmas, we show that this optimism does not survive the move from individual behavior to institutional design. First, we show that the weak Pareto optimal rule is not implementable in Berge equilibrium. Second, on the unrestricted domain of strict preferences, any social choice rule that is both weakly Pareto efficient and implementable in Berge equilibrium must be dictatorial. Finally, we show that Berge implementability is strictly more demanding than Nash implementability: every social goal achievable under strategic altruism is also achievable under self-interest, but not vice versa. The very feature that makes Berge equilibrium appealing at the behavioral level, that is, opponents protecting each agent's payoff, is precisely what forecloses efficient, non-dictatorial institutions at the design level.

Artificial intelligence (AI) interacts with climate in various ways, while a unified analytical framework of this intricate interplay is lacking. To align AI investment with climate policy, we propose such a framework integrating AI's impact on emissions, output, and climate damages into the DICE model. We distinguish between ICT-like and Industrial Revolution (IR)-like AI prospects. Calibrated to the best available evidence, we find that AI development is net polluting. Under current low abatement, ICT-like AI adds 0.1 degree C to 2100 warming, while IR-like AI adds 0.8 degree C. The associated climate costs offset roughly one-fifth and one-quarter of AI's economic gains, respectively. Meeting the 2 degree C target saves the optimal ICT(IR)-like AI investment rate by 2100 from 3.3% (5.1%) under the low-abatement scenario to 3.7% (12.7%), indicating that mitigation is complementary to AI development. We further show that the investment trade-off between AI and abatement is driven primarily by AI's economic prospects, not by its emissions footprint.

We consider fair allocation of indivisible goods in a setting in which agents have subjective valuation functions over the set of goods, and in addition, goods may be sold at given market prices. In this setting, a fair allocation involves {deciding which goods to sell, how to allocate the unsold goods, and how to divide the money received from the sold goods.} We adapt to this setting the definitions of share-based fairness notions, such as the maximin share (MMS) and the truncated proportional share (TPS), and comparison-based fairness notions such as EF1 and EFX (which we adapt to SEF1 and SEFX). We show the following results when the utility of each agent is additive both over goods and over money. With two agents, there are allocations that are simultaneously MMS and SEFX. With three agents, there are instances in which no allocation gives every agent more than $\frac{11}{12}$-MMS. With any number of agents, there are $\frac{2}{3}$-MMS allocations. There also are allocations that are simultaneously SEFX and $\frac{n}{2n-1}$-TPS. This latter ratio is best possible, even without the SEFX requirement.

We study the strategic impact of ambiguity through the channel of higher-order beliefs. We show that even small amounts of prior ambiguity about game payoffs can generate arbitrarily large amounts of higher-order ambiguity. This gives rise to a novel form of contagion: vanishingly small payoff ambiguity can select “secure” actions (e.g., non-participation) as the unique equilibrium outcome even when those actions are almost dominated. We highlight two main implications. First, classical robustness results under probabilistic uncertainty break down under ambiguity when players can deviate to secure actions. Second, if a designer can introduce small amounts of payoff ambiguity into a game, this can serve as a powerful tool for unique implementation.

We examine how macroeconomic and geopolitical developments in the United States, the euro area, and China affect the currency composition of central banks' foreign exchange reserves. Using an unbalanced panel of reserve shares for 53 countries over 1999-2023, we estimate a constrained system of equations that explicitly imposes the adding-up restriction on reserve shares. The results indicate substantial persistence in reserve holdings and significant cross-currency dependence, supporting a system-wide dynamics of reserve composition. In the country fixed-effects specification (1) issuer economic size, (2) uncertainty, (3) sanctions, (4) trade linkages, and (5) issuer credibility are significantly associated with reserve allocation across currencies. With the inclusion of year fixed effects, the persistence and cross-currency dependence remain, while trade linkages and sanctions emerge as the most important determinants of reserve composition. The results highlight the importance of accounting for the compositional nature and interdependence of reserve shares when examining the determinants of global reserve holdings.

We study auctions with costly entry in which bidders have incomplete information about their affiliated private valuations prior to entry. We examine whether indicative bidding–a mechanism requiring non-binding preliminary bids before entry–can stimulate participation and improve entrant selection, and we compare its performance with unrestricted and capped entry in a controlled laboratory experiment. When entry costs are high, indicative bidding generates significantly more revenue, primarily by increasing participation beyond theoretical predictions. When entry costs are low, its predicted revenue advantage is attenuated by higher-than-predicted selection inefficiency.

This article surveys two deeply connected literatures that approach the same fundamental problem from different disciplinary traditions: dynamic discrete choice (DDC) in structural econometrics and inverse reinforcement learning (IRL) in machine learning. Both seek to infer the preferences of decision makers from observed sequential behavior, assuming that individuals act to maximize an expected reward function within a dynamic, uncertain environment formalized as a Markov decision process (MDP). Despite independent origins, the two fields have converged on similar mathematical formulations. We show that the (soft Q-learning) framework now prevalent in IRL is closely related to DDC models under additive extreme value preference shocks, yielding the same softmax (multinomial logit) choice probabilities and smooth Bellman equations that underpin structural estimation in economics. We compare the estimation and computational methods developed in each field. DDC has emphasized maximum likelihood estimation, conditional choice probability estimators, and policy iteration methods. IRL has developed scalable alternatives, including maximum entropy methods, adversarial approaches, and model-free temporal difference estimators that extend to high-dimensional state spaces using deep neural networks. Model-free IRL estimators that combine temporal difference learning with classical two-step methods from econometrics represent a promising direction for bridging the two literatures. Both fields confront shared foundational challenges: the identification problem, whereby multiple reward functions can rationalize the same observed behavior, and the curse of dimensionality in solving the underlying MDP. We believe that cross-fertilization offers substantial opportunities for methodological progress in both fields.

We study how competition among biased news sources affects information and welfare when sources can misrepresent facts at a cost. Monopolistic and competitive market structures admit many equilibria. We develop a common belief-based selection criterion that applies to both and makes welfare comparisons possible. Under the refined outcomes, adding an oppositely biased source improves the receiver's welfare when that source faces sufficiently high misreporting costs. Competition disciplines the incumbent while introducing a few distortions of its own. Better information need not increase total welfare, and distorted advice from a monopolist can raise total welfare. Competition may improve decision-making without being socially beneficial.

Platforms use rating bonuses and badges to reward good sellers. When ratings can be inflated, stronger rewards can instead weaken the information available to buyers. We study an initially informative rating system in which sellers can pay to raise their displayed score and buyers rationally account for manipulation. Manipulation costs and returns are common across qualities conditional on the honest rating. Under this restriction, credibility can fail to increase between adjacent scores only where the higher score earns an additional reward. Our main result concerns large top-rating bonuses. Once sellers choose only between keeping their honest rating and reaching the top, a larger bonus makes the equilibrium rating system less informative in Blackwell's order along either extremal equilibrium selection. Lower scores retain their honest quality composition but become rarer. As the bonus grows, almost everyone displays the top score, its credibility approaches the population average, and the value of rating information vanishes. Nevertheless, a nearly perfect score can remain more credible than a perfect one. We derive the rate of information loss for exponential manipulation costs and establish a budget-feasible exposure rule that avoids credibility reversals. The results suggest that platform evaluations should examine rating frequencies together with category credibility. Rational buyer skepticism can correct the interpretation of ratings without recovering the distinctions that manipulation has erased.

We study dynamic linear panel data models in which the lagged outcomes and strictly exogenous covariates carry individual-specific coefficients and the time-varying errors have a flexible covariance structure. With a fixed number of time periods, we point-identify the joint distribution of the random coefficients and the structural errors under a distributional form of strict exogeneity, and propose a closed-form, multi-step estimator based on the inverse Radon transform. We establish a uniform convergence rate for the estimator of the random coefficient density, as well as uniform consistency of the estimator for the conditional density of the time-varying structural errors. Monte Carlo simulations demonstrate good finite-sample performance of the estimators.

Econometric models offer parsimonious but inexact approximations to data-generating processes. This paper studies the generalized method of moments (GMM) when exchangeable specification errors of order $n^{-1/2}$ contaminate the moment conditions. I develop estimators for the mean and variance of these specification errors, establishing their consistency in an asymptotic framework where the number of overidentifying restrictions grows with the sample size. These hyperparameter estimates are used to develop a feasible bias-corrected estimator of target parameters. I also propose an empirical Bayes estimator that weakly improves precision by subtracting a best linear predictor of the first-order estimation error from the bias-corrected estimator. Using a combinatorial central limit theorem, I establish asymptotic normality of both estimators and provide variance estimators that enable misspecification-aware frequentist inference. Simulation exercises indicate the procedures can meaningfully improve on standard two-stage least squares estimation when exclusion violations are present. Revisiting the influential study of Angrist and Krueger (1991), I consider an instrument set where exchangeable excludability violations are plausible. Repairing the two-stage least squares estimates of the returns to schooling moves them in the direction of ordinary least squares and reduces sensitivity to the specification of controls.

This non-technical survey introduces the topic of groundwater management to the mathematics and statistics communities. We focus on the emerging marketplaces for groundwater pumping rights, outlining probabilistic and statistical approaches to groundwater dynamics and game equilibria that underpin stakeholder decisions. After discussing rationing schemes and regulatory policies we list a range of open problems that are well suited to be tackled by the mathematical sciences researchers.

Problem definition: Solar electricity generation is a strategic component of energy portfolios designed to meet growing demand and reduce carbon emissions. Governments and municipalities encourage household photovoltaic (PV) adoption through upfront rebates and tax credits. Limited budgets require principled, data-driven policies that account for the drivers of adoption and the effects of incentives on adoption rates. Methodology/results: We develop a dynamic structural model of residential PV diffusion based on adoption decisions by forward-looking households that weigh the economic trade-offs between installing now and later. Adoption depends on return on investment and influence from neighboring adopters. The model segments households by home value and urbanization level, incorporates unobserved heterogeneity, and captures spatiotemporal installation dynamics. We estimate the model using Bayesian methods and detailed household-level data from Austin, Texas. In out-of-sample tests, it predicts installations more accurately than contemporary alternatives. We simulate counterfactual policies within the dynamic equilibrium of PV diffusion to evaluate rebate designs. The framework can also be adapted to study the adoption of other durable technologies. Managerial implications: A rebate offered for a limited period generates more adoption and emissions reductions than a prolonged, costlier program. This counterintuitive result arises from forward-looking behavior, neighbor influence, and accelerated adoption before the rebate expires. We also evaluate phased reductions and rebates differentiated by household segment. A two-step reduction outperforms multiple small reductions. Geographic differentiation improves policy performance, whereas differentiation by home value offers little advantage over a uniform rebate.

We study deterministic choice when the same decision maker is observed before and after a deliberative intervention that makes one of two fixed ordinal consequence dimensions more salient. Both choice modes are path independent. Ordinary choice respects two-dimensional dominance, while any binary reversal under fuller deliberation must favor the alternative that is strictly better on the emphasized dimension. We characterize exactly the admissible ordinary and full-horizon rankings. The characterization yields a protected partial order, sharp pairwise and menu-level identification, a signed restriction on menu-level choice changes, and an acyclicity test for incomplete observations. With an ordered sequence of deliberative modes, choices from any fixed menu form a monotone tradeoff path: every switch improves the emphasized dimension, worsens the other dimension, and an abandoned alternative cannot reappear. When the emphasized consequence order is strict, the admissible rankings have an equivalent Kemeny-Kendall representation. The framework is ordinal: it requires neither a cardinal tradeoff nor lexicographic priority, and it applies whenever deliberation gives greater emphasis to one of two fixed consequence dimensions.

This paper develops a procedure for uncovering the common cyclical factors that drive a mix of stationary and nonstationary variables. The method does not require knowing which variables are nonstationary or the nature of the nonstationarity. An application to the FRED-MD macroeconomic dataset demonstrates that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.

Artificial intelligence (AI) is transforming measurement in economics. AI models convert unstructured data, such as text and images, into structured variables at low cost, making previously prohibitive measurement feasible at scale. This shifts the bottleneck from finding any scalable measure of a phenomenon to choosing among many plausible ones, which may support different empirical conclusions. This review provides guidance for navigating that shift. We describe three stages at which AI enters the measurement pipeline—discovery, construct definition, and observation—and what each demands of researchers. We argue that credible inference with AI-generated variables requires appropriately designed validation: anchoring measurement to explicit criteria, rather than informal claims that a proxy is reasonable. We then examine how validation samples support valid inference even when AI predictions are arbitrarily biased, and what can be done when a random validation sample is unavailable.

In panels with sample selection (that may occur due to attrition, nonresponse, etc.), the assumption of selection on observables (missing at random, MAR) is commonly imposed despite often being implausible. However, this assumption becomes testable when a refreshment sample is available. We develop a statistical test of MAR based on a distance between two estimated distributions: one obtained using the standard inverse probability weighting (IPW) that is valid under MAR and the other obtained using an alternative weighting that is valid under a weaker assumption of additive nonignorability of Hirano et al. (2001). This test implicitly compares the distribution of the IPW-weighted sample in the attrition period with the distribution of the refreshment sample, which coincide if the MAR assumption holds. We establish that, when the input distributions are parametric, our test statistic converges to the generalized chi-squared distribution under the null of MAR. This limit distribution can be estimated using the recursive formulas derived by Franguridi et al. (2026). We illustrate the performance of our test in Monte Carlo simulations. Finally, we apply our test to an empirical example using a subsample of the Understanding America Study (UAS) dataset.

Research, policy, and capital rely on the Cooperative Patent Classification's Y02 tag to locate climate invention, yet whether Y02 measures what it is used to measure has never been tested at corpus scale. We ask three questions: what types of error does Y02 make, are those errors idiosyncratic, and what are their main causes? Because neither Y02 nor a text classifier trained on it is ground truth, our Error-as-Signal framework treats their disagreements as evidence, not model failure: in a construct-validity test, two independent large language models judge whether the primary function of each of 517,772 disputed inventions among 9,075,421 USPTO patents has a direct climate mechanism. Y02 makes two types of error, which we term False Green (180,384 inclusions) and Silent Green (29,465 omissions). The errors are not idiosyncratic but form a systematic bias: False Green concentrates in ICT, Silent Green in energy, transport, chemistry, and industrial engineering, so raw counts overstate ICT energy efficiency threefold. The evidence points away from applicant strategy, misclassification did not jump when green labels became salient, and toward bounded classification capacity. Yet complexity does not simply make classification harder; its two forms push errors in opposite directions, a complexity paradox. A one-standard-deviation increase in reflection complexity (capability scarcity) is associated with 2.45 times the odds that an error is an omission rather than an inclusion, whereas structural complexity (combinatorial diversity) tilts errors toward inclusion. What matters is not how complex an invention is but whether its complexity is legible to the taxonomy.

We develop a unified approach to optimal grading in an all-pay contest in which a designer assigns a fixed vector of heterogeneous prizes to maximize expected total effort. The approach covers two information regimes and identifies a common principle: iron locally misordered incentive returns and assign prizes assortatively across the resulting grades. Under rank-only grading, assignments depend only on ordinal ranks. Ironing cumulative rank coefficients—via the least concave majorant or the pool-adjacent-violators algorithm—determines which adjacent ranks are pooled and which prizes are randomized within each grade. Under performance-contingent grading, assignments may depend on numerical effort. The optimum irons virtual ability, forms endogenous type grades, and assigns prize blocks assortatively across grades. A failing grade below a minimum passing effort and a collection of effort brackets implement the direct optimum while preserving full prize assignment.

Constitutions as the formal foundation of a country's legal and political system have important economic and political effects. Yet, we still know little about why constitutions set effective constraints on politicians in some societies, while being largely disregarded in others. Here, we ask if national culture matters for constitutional compliance. We study a cross-section of 115 countries, making use of novel indicators of constitutional compliance. We find that societies with a more individualistic population exhibit higher levels of compliance. These results are robust and extend to instrumental variables estimations. They imply a novel transmission channel from cultural traits to long-term economic development: individualistic national culture increases the credibility of constitutional self-commitments. Our analysis also supports the more general idea that the effects of formal institutions depend on the informal institutional environment in which they are embedded. Regarding religion, our results are consistent with past research that attributes the lack of development in the modern Muslim world to deficient institutional quality.

Social scientists rely on hypothesis testing to support their research conclusions, but the standard tests are designed for testing one hypothesis rather than adjudicating between rival possibilities. We develop a new framework, "classification testing", as an alternative. Instead of selecting one hypothesis to test, a researcher conducting a classification test decides what qualitative distinctions ("classes") are most substantively relevant; the test either assigns the estimand to a class with error control similar to that of a conventional hypothesis test, or declares the result inconclusive. We argue that classification testing is superior to current practice not just when the objective is to adjudicate between rival possibilities but also when there is one research hypothesis to be tested, because classification testing exposes that hypothesis to refutation. We illustrate the framework by applying it to a well-known media experiment and offer an R package to aid in implementation.

In the empirical sciences, significance thresholds often determine whether findings are treated as evidence of an effect. This paper studies how likely findings that just meet conventional significance thresholds are to remain significant in replications of the same sample size. To answer this question, we estimate the expected replication probability conditional on a given p-value among published studies for experimental economics, psychology, and social science. We validate this measure by showing it accurately predicts actual replication outcomes, outperforming prediction markets. A finding with a p-value of 0.05 has an expected replication probability ranging from 0.10 to 0.25 across fields. Low replicability reflects low power in original studies rather than publication bias. We then develop a nonparametric estimator and apply it to economics literatures that use larger samples, finding higher but still low replication probabilities. These results indicate that statistical significance in a single study provides only suggestive evidence of an effect. Stronger conclusions require cumulative evidence.

Traditional statistical tests are poorly suited for the sequential evaluation of probabilistic forecast calibration. We address this limitation in macroeconomic forecasting by applying a new sequential testing method based on e-values. The e-value-based methodology enables anytime-valid inference. It allows practitioners to test against calibration continuously without invalidating statistical guarantees. To illustrate the framework's practical value, we apply it to probabilistic inflation forecasts for the United States, the Euro Area, and Switzerland. Our analysis shows that the sequential approach gives detailed insights into the timing and nature of forecast misspecification. We find these diagnostics are particularly insightful during major structural breaks. During these events, we find evidence against calibration that static, full-sample tests often miss. Therefore, this work shows that e-value-based tests are a practical method for the evaluation of forecast calibration in empirical macroeconomics.

What happens to a party's vote share when it nominates a woman for executive office? The evidence on female candidates is dominated by legislative races and by designs that pool parties and periods into a single average effect, which is typically null. This paper builds a new county-level panel of US gubernatorial elections - 578 races in 49 states from 1980 to 2024, reconstructed and audited from primary sources - and estimates party-specific, time-varying nomination effects with a stacked difference-in-differences design around within-state transitions from male to female nominees. The average null conceals a sharp asymmetry. Republican transitions to a female nominee since 2016 are followed by a county-level vote-share decline of 6.5 percentage points, robust across cohort, weighting and inference checks. No comparable average differential is detected for Democratic nominees. Survey-ballot diagnostics are compatible with demand-side discrimination but cannot identify that channel.

Understanding the propagation of extreme events is important in many economic and environmental applications, yet most econometric methods for causal inference focus on average effects rather than tail behavior. This paper studies the identification of causal relations in extremes and derives resulting estimators and their asymptotic inference. As measure of causal dependence between extreme realizations of variables, we analyze the asymptotic behavior of the Causal Tail Coefficient (CTC) within a linear structural causal model with heavy-tailed regularly varying innovations. In contrast to the existing literature, we allow the variables in the system to exhibit heterogeneous tail indices and consider the presence of potentially heavy-tailed confounders. We derive theoretical results assessing the limiting behavior of the CTC under these conditions and show how differences in tail behavior can help to reach identification of the causal structure. Light-tailed confounders are asymptotically negligible, but sufficiently heavy-tailed confounders can induce extremal dependence patterns that are observationally indistinguishable from direct causal effects. When suitable proxy information is available, identification can be recovered using an adjusted Causal Tail Coefficient. Based on these results, we develop estimation and inference procedures for causal relations in extremes under general conditions. We establish asymptotic properties of the proposed estimators and derive tests for the causal direction and heavy-tailed confounding. Simulation evidence examines their finite-sample performance and provides guidance on their implementation. Applications to climate and financial extremes illustrate how the proposed methods can uncover causal relations that may remain undetected by approaches targeting average dependence.

Analysis of experimental data becomes challenging when the underlying population is connected by a network. Exposure mapping is a common tool in the literature for defining and estimating spillover effects. These mappings reduce the dimensionality of the estimand, thereby facilitating identifiability. It is assumed that this mapping is correctly specified, leaving the choice of the exposure mapping to the analyst. This makes estimators of the spillover effect, such as the Horvitz-Thompson estimator, vulnerable to bias from model misspecification. Although these estimators have been shown to be robust to certain forms of controlled misspecification, there has been relatively little methodological progress in empirically investigating appropriate exposure mappings. In this paper, we propose a novel design-based model specification framework for causal inference. Building on this, we develop a randomization-testing procedure to assess the correct specification of an exposure-mapping model in the presence of network interference. We provide theoretical guarantees for the asymptotic validity of the proposed testing procedure. We establish the favorable power properties of our method through an extensive simulation study and illustrate it in a field experiment investigating the effect of anti-conflict norms among adolescents.

Spatial autoregressive inference is typically conditional on the spatial weights matrix, W, even though the underlying interaction structure is often unknown and empirical conclusions can be sensitive to its specification. This paper develops double/debiased machine learning inference for low-dimensional SAR parameters when the spatial interaction operator is learned flexibly from potentially endogenous characteristics. Within a maintained admissible support, interaction strength is generated by an unknown function of geographic and socioeconomic characteristics, making inference robust to functional form specification of the weights within that support. Endogeneity in the characteristics generating W is addressed through a nonlinear control function based on locally relevant first-stage residual information. Because the learned operator enters both the spatial lag and spatially transformed instruments, treating the estimated W as known generally leaves a first-order generated-W effect. I construct an operator-orthogonal SAR-IV/GMM score that removes this leading sensitivity and combine it with buffered spatial cross-fitting that separates evaluation-score footprints from nuisance-training observations. Under near epoch dependence on a spatially mixing innovation field and target-relevant nuisance rate and regularity conditions, the estimator is asymptotically linear and root-n normal. Monte Carlo simulations show improved finite-sample inference relative to nonorthogonal alternatives when the interaction function is misspecified, weight generating characteristics are endogenous, and observations are spatially dependent. In a U.S. application, diabetes estimates vary with the choice of W, showing the sensitivity of SAR inference to the interaction structure. Even for the same learned W, results differ across inferential methods, highlighting the importance of inference when W is learned.

When shopping is delegated to AI agents, it is unclear whether the ranking advantage documented for humans persists. Across 7,000 sessions with five large language models (LLMs) and varying reasoning effort, we compare AI agents with human field data. AI agents search more extensively than human consumers. As with humans, lower-ranked listings are less likely to be inspected, although the effect is smaller for AI agents. Unlike humans, AI agents show a pattern consistent with the lost-in-the-middle effect, whereby middle listings have the lowest probability of inspection. At the choice stage, position effects are concentrated at lower reasoning effort, but higher effort reduces the middle penalty for every LLM that exhibits it. These findings suggest that, when search is delegated, displayed attributes may matter more than placement, and that exposure to position bias depends on how the AI agent is configured.

A team-contest designer values output rather than expenditure; nonlinear conversion makes the distinction consequential. We study two non-pecuniary instruments in majority-rule contests decided by pairwise all-pay battles with private abilities: disclosing resolved outcomes and splitting the battle schedule into finer blocks. Neither changes a battle's average pivotality; each only redistributes it across histories. Under nested information structures, this redistribution makes equilibrium ability-scaled expenditure weakly more dispersed player by player without changing its mean; expected aggregate expenditure is invariant across all designs considered. The resulting convex-order comparison ranks expected total output: convex output costs favor no disclosure and coarser temporal structures, concave costs favor full disclosure and finer ones, and linear costs make both comparisons neutral. The rankings hold for finite-support and smooth continuous-type ability distributions. The mechanism extends to degree-zero component contest technologies with a unique equilibrium outcome distribution. No and full disclosure bound every admissible public garbling.

During clinical trials evaluating a drug's effect on a survival endpoint, intermediate events often occur in addition to the primary event. The treatment can exert its effect on the primary endpoint along multiple pathways through intermediate events. Assumptions for identifying mediation effects, such as sequential ignorability in natural effects or the dismissible components condition in separable effects, fail because intermediate events act as treatment-induced confounding. To understand the effect along each pathway, we consider hypothetical interventions in transitions between event statuses to mimic the treatment mechanism. The hypothetical interventions adjust for effects through intermediate events and marginalize over unobserved treatment-induced confounding, if any. Based on the derived efficient influence functions for the counterfactual cumulative incidences under hypothetical interventions, we construct multiply robust and semiparametrically efficient estimators for pathway treatment effects. Our proposed framework enables the examination of treatment effects through each transition, on each event, and along each path. By analyzing data from the LEADER Trial, we find that liraglutide significantly reduces the risk of cardiovascular and microvascular events. The reduction in all-cause mortality is primarily mediated by its effects on expanded major adverse cardiovascular events.