B1352
Title: Valid causal inference when using deep convolutional neural networks to control for highly structured covariates
Authors: Mohammad Ghasempour - Umea University (Sweden) [presenting]
Niloofar Moosavi - Umeå university (Sweden)
Xavier de Luna - Umea University (Sweden)
Abstract: Convolutional neural networks (CNN) have been successful in machine learning applications including image classification. When it comes to images, their success relies on their ability to consider the space invariant local features in the data. We consider the use of CNN to fit highly dimensional nuisance models in semiparametric estimation of a one-dimensional causal parameter: the average causal effect of a binary treatment. In this setting, nuisance models are functions of pre-treatment covariates that need to be controlled for. In an application where we want to estimate the effect of early retirement on a health outcome, we propose to use CNN to control for highly dimensional and time-structured covariates. Thus, CNN is used when fitting nuisance models explaining the treatment assignment and the outcome. These fits are then combined into an estimator having a nonparametric doubly robust property. Theoretically, we contribute by providing rates of convergence for CNN equipped with the rectified linear unit activation function and compare it to an existing result for feedforward neural networks. We also show when those rates guarantee uniformly valid inference for the proposed doubly robust estimator. A Monte Carlo study is provided where the performance of the proposed estimator is evaluated and compared with other strategies. Finally, we give results on a study of the effect of early retirement on later hospitalization using a database on the Swedish population.