Economic mechanisms administer the allocation of resources to interested agents based on their self-reported types. One objective in mechanism design is to design a strategyproof process so that no agent will have an incentive to misreport its type. However, typical analyses of the incentives properties of mechanisms operate under strong, usually untestable assumptions. Empirical, data-oriented approaches are, at best, under-developed. Furthermore, mechanism/policy evaluation methods usually ignore the dynamic nature of a multi-agent system and are thus inappropriate for estimating long-term effects. We introduce the problem of estimating the causal effects of mechanisms on incentives and frame it under the Rubin causal framework \citep{rubin74, rubin78}. This raises unique technical challenges since the outcome of interest (agent truthfulness) is confounded with strategic interactions and, interestingly, is typically never observed under any mechanism. We develop a methodology to estimate such causal effects that using a prior that is based on a strategic equilibrium model. Working on the domain of kidney exchanges, we show how to apply our methodology to estimate causal effects of kidney allocation mechanisms on hospitals' incentives. Our results demonstrate that the use of game-theoretic prior captures the dynamic nature of the kidney exchange multiagent system and shrinks the estimates towards long-term effects, thus improving upon typical methods that completely ignore agents' strategic behavior.
CEPR Paris Symposium（CEPR巴黎研讨会）；Metascience（元科学）；NetSciSci（网络科学）；EAYE；Groningen；AYEW；ZBW；MPWZ-CEPR Text-As-Data（MPWZ-CEPR文本作为数据）；PolMeth；Leibniz Open Science（莱布尼茨开放科学）；Causal Data Science Meeting（因果数据科学会议）
Causal mediation analysis for zero-inflated longitudinal data in the presence of treatment non-compliance and multiple mediators
University of Chicago（芝加哥大学）；Smeal College of Business, The Pennsylvania State University（宾夕法尼亚州立大学斯米尔商学院）；University of Florida（佛罗里达大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
Assessing the impact of tourist attractions through the integration of causal inference and demand-side economic analysis: A case study of the Sensoria experience museum in Holzminden, Germany
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
1 Department of Multidisciplinary Engineering, Texas A\&M University, College Station, TX, USA；2 Department of Statistics；3 Department of Computer Engineering, KNUST, Kumasi, Ghana；4 Department of Computer Science, George Washington University, Washington, DC, USA；5 Department of Engineering Technology；Industrial Distribution, Texas A\&M University, College Station, TX, USA
Economics of Semantic Communication System: An Auction Approach
Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-南洋理工大学新加坡联合研究院）；Nanyang Technological University（南洋理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly（南洋理工大学-不列颠哥伦比亚大学老年活力生活卓越联合研究中心）；Sungkyunkwan University（成均馆大学）
Incentive Mechanism Design for Resource Management in Satellite Networks: A Comprehensive Survey
Phenikaa University（phenikaa大学）；University of Essex（埃塞克斯大学）；University of New South Wales（新南威尔士大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Zhejiang Gongshang University（浙江工商大学）；Nanyang Technological University（南洋理工大学）
Copenhagen Causality Lab and Pioneer Centre for AI, University of Copenhagen, Denmark（哥本哈根因果实验室和先锋人工智能中心，哥本哈根大学，丹麦）；Cooperative AI Foundation, United Kingdom（协作人工智能基金会，英国）
