【日時】
2025年1月11日（土）13:00～17:20（受付開始 12:30～）

【場所】
京都大学芝蘭会館・稲盛ホール（定員 200名）

【講演者】
・基調講演：「激動の世界と政策対応」
　神田 眞人　内閣官房参与
・講演：「地域での脱炭素と経済社会課題の同時解決を目指して」
　大森 恵子　環境省大臣官房地域脱炭素推進審議官
・講演：「社会関係資本と住民主体のまちづくり活動」
　要藤 正任　京都産業大学経済学部教授
・講演：「先端政策分析の意義：国際機関の現場から」
　八代 尚光　国際通貨基金シニアエコノミスト　※録画
・パネルディスカッション：「医学×社会科学による文理融合研究（Socio-Life Science）から政策を提起する」
　松田 文彦　京都大学医学研究科教授
　関根 仁博　産業技術総合研究所企画本部審議役
　井上 祐介　京都大学経済研究所先端政策分析研究センター特定准教授（コーディネーター）
・パネルディスカッション：「『社会実装』がSPI（science and policy interface）を切り拓く」
　松本 眞　尼崎市長
　大竹 文雄　京都大学経済研究所特定教授/ 大阪大学感染症総合教育研究拠点特任教授
　谷 直起　京都大学経済研究所先端政策分析研究センター特定准教授（コーディネーター）

【詳細】
聴講は無料（申込制）
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Flyerはこちら

アブストラクト：While average treatment effects are a common focus in causal inference, such measures often mask important distributional characteristics of counterfactual outcomes. This work considers the estimation of counterfactual densities under continuous treatments, thereby allowing richer and more detailed insights into the effects of interventions. We propose a Neyman-orthogonal moment condition that treats the conditional outcome density and the generalized propensity score as nuisance parameters. Leveraging this orthogonality within a debiased machine learning (DML) framework ensures the asymptotic normality of the parameter of interest, even when employing flexible machine learning methods for nuisance estimation. However, two challenges arise in finite samples due to the structure of the proposed moment conditions. First, the double summation within the moment conditions makes standard cross-fitting approaches susceptible to poor estimation performance, especially in small- or medium-sized datasets. To address this, we derive theoretical conditions under which DML can be implemented without sample splitting, thus mitigating performance degradation. Second, the proposed moment conditions involve integral over the nuisance estimates, meaning numerical integration errors can negatively affect estimation accuracy. Hence, it is desirable to use nuisance estimators that allow for easy analytical integration. As an illustrative example, we employ random forests as the nuisance estimator to satisfy these two requirements. We demonstrate the effectiveness of the proposed method through simulation studies.

Abstract：(Tentative) We draw on new granular data from cities around the world to study how the spatial distribution of income within cities varies with development. We document that in less-developed countries, average incomes of urban residents decline monotonically in distance to the city center, whereas income-distance gradients are flat or increasing in developed economies. We also show that urban neighborhoods with natural amenities – in hills and near rivers – are poorer than average in lessdeveloped countries and richer than average in developed ones. We hypothesize that these patterns arise due to the differences in the provision of residential and transportation infrastructure within cites. Using a quantitative urban model, we show that observed differences in residential and transportation infrastructure help explain a significant fraction of how the spatial income distribution within cities varies with income per capita.