日時：2026年5月28日(木)13：30～15：00

会場：豊中キャンパスOSIPP棟6階　会議室

講師：白糸 裕輝氏(University of Michigan) 　 

報告論題と要旨につきましては決まり次第あらためてご案内いたします。

-----------------------------------------

  Time and Date:  13：30～15：00, Thursday, May. 28th

  Seminar Venue: 会議室 6F OSIPP棟, Toyonaka Campus

  Presenters:  Yuki Shiraito (University of Michigan)

  Title and Abstract : TBA

The seminar title and abstract will be announced once they are finalized.

日時：2026年6月4日(木)15：00～16：30

会場：豊中キャンパスOSIPP棟6階　会議室

講師：柳本 和春氏（神戸大学） 

タイトル：TBA　

※本セミナーは以下の科学研究費助成事業による共催で開催されます。

基盤研究（A）課題番号：26H01962

「出生時格差：『親ガチャ』が人生を制限しない社会を目指して」

（研究代表者：丸山士行教授）

日時：2026年6月25日(木)13：30～15：00

会場：豊中キャンパスOSIPP棟6階　会議室

講師：Jonathan Smith氏（Georgia State University） 

タイトル：TBA

日時：2026年7月2日(木)13：30～15：00

会場：豊中キャンパスOSIPP棟6階　会議室

講師：熊谷 元宏氏（オーストラリア国立大学） 

タイトル：TBA

日時：2026年7月23日(木)13：30～15：00

会場：豊中キャンパスOSIPP棟6階　会議室

講師：大越　裕史氏（岡山大学） 

タイトル：TBA

日時：2026年9月10日(木)10：30～12：00

会場：豊中キャンパスOSIPP棟6階　会議室

講師：奥山　陽子氏（ウプサラ大学） 

タイトル：TBA

日時：2027年3月23日(火)13：30～15：00

会場：豊中キャンパスOSIPP棟6階　会議室

講師：Jason Sockin氏  （Cornell University） 

タイトル：TBA 

日時：2026年4月8日（水）13：00～15：00（日本時間）☆Zoomでのオンラインミーティングで開催です☆

報告者：Undral Byambadalai 氏　(National University of Mongolia)

議題：Beyond the Average: Distributional Causal Inference under Imperfect Compliance

アブストラクト：We study the estimation of distributional treatment effects in randomized experiments with imperfect compliance. When participants do not adhere to their assigned treatments, we leverage treatment assignment as an instrumental variable to identify the local distributional treatment effect—the difference in outcome distributions between treatment and control groups for the subpopulation of compliers. We propose a regression-adjusted estimator based on a distribution regression framework with Neyman-orthogonal moment conditions, enabling robustness and flexibility with high-dimensional covariates. Our approach accommodates continuous, discrete, and mixed discrete-continuous outcomes, and applies under a broad class of covariate-adaptive randomization schemes, including stratified block designs and simple random sampling. We derive the estimator's asymptotic distribution and show that it achieves the semiparametric efficiency bound. Simulation results demonstrate favorable finite-sample performance, and we demonstrate favorable finite-sample perfomance, and we demonstrate the method's practical relevance in an appication to the Oregon Health Insurance Experiment.

日時：2026年4月16日（木）15：10～16：40

 場所：豊中キャンパス　法経研究棟7階　小会議室

 講師：森田 公之氏（専修大学経済学部）

タイトル：The Allocation of Decision Authority in Three-Stage Decision Processes with Applications to Artificial Intelligence in Organizations

アブストラクト：We study a three-stage decision process that consists of information acquisition,

project choice, and execution of the selected project. A principal wants to choose and implement a proactive project, and hires an agent who chooses a costly effort at the information acquisition stage as well as a costly effort at the execution stage. What the principal can do at the beginning is the allocation of the formal decision authority over project choice, either to herself or the agent. We show that the principal may choose to delegate decision authority to the agent, however unlikely the interest of the agent is to be congruent with her interest, or however competent and experienced she is. We provide several testable predictions. (i) Delegation is more likely as the manager has discretion over both information acquisition and implementation. (ii) Delegation is less likely as opportunities for compromising improve. (iii) Whether or not the parties agree about the status quo matters: In particular, if their preferences about the default decisions differ, the organization is more likely to be decentralized for new project development as their interests are less likely to be congruent. We further discuss the extent to which our results on optimal delegation survive when artificial intelligence (AI) is deployed, distinguishing autonomous and nonautonomous AI. If AI can fully automate information acquisition or execution, delegation cannot be optimal, but it

can be optimal if the agent remains responsible for execution. If AI instead supports execution by lowering its cost, delegation can survive.

日時：2026年4月23日（木）13：30～15：00

 場所：豊中キャンパス OSIPP棟6階　会議室

 講師： Alan Spearot氏( University of California, Santa Cruz)　　

 タイトル："Predicting Welfare Effects of Trade Shocks using Equity Markets: Theory and Evidence from Liberation Day"