Causal inference vía artificial neural networks

时间:2021-10-12         阅读:


Causal inference vía artificial neural networks

主讲人美国加州大学河滨分校 马舒洁教授

主持人统计学院 林华珍教授


直播平台及会议ID腾讯会议,ID: 561 165 357

主办单位:统计研究中心 统计学院 科研处


马舒洁,现为加州大学河滨分校统计系正教授。于密歇根州立大学统计与概率系获得博士学位。现担任 Journal of the American Statistical Association, Journal of Business & Economic Statistics等多个统计类学术期刊副主编。她目前研究兴趣包括大规模数据分析,精准医疗,机器学习,网络数据分析以及非参数和半参数推断。先后在统计学和经济学国际学术期刊上发表四十余篇学术论文。



Recent technological advances have created numerous large-scale datasets in observational studies, which provide unprecedented opportunities for evaluating the effectiveness of various treatments. Meanwhile, the complex nature of large-scale observational data post great challenges to the existing conventional methods for causality analysis. In this talk, I will introduce a new unified approach that we have proposed for efficiently estimating and inferring causal effects using artificial neural networks. We develop a generalized optimization estimation through moment constraints with the nuisance functions approximated by artificial neural networks. This general optimization framework includes the average, quantile and asymmetric least squares treatment effects as special cases. The proposed methods take full advantage of the large sample size of large-scale data and provide effective protection against mis-specification bias while achieving dimensionality reduction. We also show that the resulting treatment effect estimators are supported by reliable statistical properties that are important for conducting causal inference.