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主讲人 |
范青亮 |
简介 |
<p>Nonlinearity and endogeneity are prevalent challenges in causal analysis using observational data. This paper proposes an inference procedure for a nonlinear and endogenous marginal effect function, defined as the derivative of the nonparametric treatment function, with a primary focus on an additive model that includes high-dimensional covariates. Using the control function approach for identification, we implement a regularized nonparametric estimation to obtain an initial estimator of the model. Such an initial estimator suffers from two biases: the bias in estimating the control function and the regularization bias for the high-dimensional outcome model. Our key innovation is to devise the double bias correction procedure that corrects these two biases simultaneously. Building on this debiased estimator, we further provide a confidence band of the marginal effect function. Simulations and an empirical study of air pollution and migration demonstrate the validity of our procedures.</p> |
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主讲人简介 |
<p>范青亮，香港中文大学经济学系副教授。2012年毕业于美国北卡罗来纳州立大学，获得经济学博士学位。主要研究领域为计量经济学。目前主要从事机器学习、因果推断、资产组合和定价预测模型的研究。在<em>Journal of the Royal Statistical Society Series B, Review of Economics and Statistics, Journal of Econometrics, Strategic Management Journal</em>等期刊发表多篇论文。</p>
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