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报告题目：Estimating Counterfactual distribution functions via Optimal Distribution Balancing with Applications

时间：2024年6月3日(星期一)8:30

地点：理工楼631报告厅

报告摘要：To avoid estimating the inverse propensity weights, which is sensitive to model specification and easily causes unstable estimates, and often fails to adequately balance covariates in finite samples, this paper proposes a new estimator of counterfactual distribution functions. We find the weights of minimum dispersion that exactly balance the estimated conditional distributions among the treated, untreated, and combined groups via a well-defined convex optimization problem. The resulting estimator converges weakly to a mean-zero Gaussian process at the usual parametric rate $\sqrt{n}$, which does not need a high-order kernel in a nonparametric estimation. Also, we show that a properly designed Bootstrap method can be used to obtain confidence intervals for conducting statistical inferences, together with its theoretical justification. With the estimates of counterfactual distribution functions, we also provide the methods and theories to estimate the quantile treatment effects and test the stochastic dominance relationship between the potential outcome distributions. Monte Carlo simulations demonstrate that our estimator performs better than the inverse propensity weighting estimators in many scenarios. Finally, our empirical study revisits the effect of maternal smoking on infant birth weight.

报告人简介

林明，北京大学应用数学博士，厦门大学经济学科教授，厦门大学经济学院副院长，全国应用统计专业学位研究生教育指导委员会委员。主要研究领域为计量经济学、贝叶斯统计，现主持一项国家自然科学基金重点项目。

甘肃应用数学中心

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2024年5月31日