開催日時：3月13日(水) 9:30 ~ 60分

開催場所：Webのみ
※　対象はデータサイエンス学系教員, 彦根地区教員, 大津地区教員, データサイエンス学系大学院生

講演者：William Torous先生（カリフォルニア大学バークレー校）

題目：An Optimal Transport Approach to Estimating Causal Effects via Nonlinear Difference-in-Differences

概要：
　We propose a nonlinear difference-in-differences method to estimate multivariate counterfactual distributions in classical treatment and control study designs with observational data. Our approach sheds a new light on existing approaches like the changes-in-changes estimator and the classical semiparametric difference-in-differences estimator, and it also generalizes them to settings with multivariate heterogeneity in the outcomes. The main benefit of this extension is that it allows for arbitrary dependence between the coordinates of vector potential outcomes and includes higher-dimensional unobservables, something that existing methods cannot provide in general. We demonstrate its utility both on synthetic and real data. In particular, we revisit the classical Card and Krueger dataset which reports fast food restaurant employment before and after a minimum wage increase. A reanalysis with our methodology suggests that these restaurants substitute full-time labor with part-time labor on aggregate in response to a minimum wage increase. This treatment effect requires estimation of the multivariate counterfactual distribution, an object beyond the scope of classical causal estimators previously applied to this data. This is joint work with Florian Gunsilius (University of Michigan) and Philippe Rigollet (MIT).

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