In this talk, I will first briefly review sieve extreme estimation procedure for general semi-nonparametric models. I will then present two recent papers on semiparametric efficient estimation of some causal parameters via ANN (an example of nonlinear sieve). The first paper is about efficient estimation of general treatment effects using ANN with a diverging number of confounders. ( a paper by Xiaohong Chen, Ying Liu, Shujie Ma and Zheng Zhang; arXiv: https://arxiv.org/abs/2009.07055). The second paper is about efficient estimation of average price elasticity and other expectation functionals of nonparametric conditional moment restriction models via ANN for time series data. (a paper by Jiafeng Chen, Xiaohong Chen, Yuan Liao and Elie Tamer). Both papers demonstrate the advantage of ANN sieves over linear sieves (such as splines) in estimating nonparametric causal models with high dimensional covariates.

Xiaohong Chen is currently Malcolm K. Brachman Professor of Economics, Yale University. Previously, she has taught at the University of Chicago, London School of Economics and New York University. Chen got her PhD in Economics in 1993 from University of California, San Diego.
Chen’s research field is econometrics. She is known for her research in penalized sieve estimation and inference on semiparametric and nonparametric models, with applications in copulas, asset pricing, missing data, nonclassical measurement errors and causal inference.
She is a winner of the China Economics Prize in 2017. She is a distinguished fellow of the Luohan Academy from 2020 to 2022. She won Econometric Theory Multa Scripsit Award in 2012, The Journal of Nonparametric Statistics 2010 Best Paper Award, The Richard Stone Prize in Journal of Applied Econometrics for the years 2008 and 2009, The Arnold Zellner Award for the best theory paper published in Journal of Econometrics in 2006 and 2007.