Convolutional neural networks (CNN) have been successful in machine learning applications. Their success relies on their ability to consider space invariant local features. We consider the use of CNN to fit nuisance models in semiparametric estimation of the average causal effect of a 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 time-structured covariates. Thus, CNN is used when fitting nuisance models explaining the treatment and the outcome. These fits are then combined into an augmented inverse probability weighting estimator yielding efficient and uniformly valid inference. 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. 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 hospitalization using data covering the whole Swedish population.

Pepper the robot is taking early retirement. The humanoid's maker, Japan's SoftBank Group, has reportedly stopped producing Pepper due to weak demand. Pepper had been touted as the harbinger of a robotics revolution, but the droid's early demise show it couldn't quite live up to the hype. Attend the tech festival of the year and get your super early bird ticket now! Pepper nonetheless made a mark on the public during the android's six-year run.

Will early retirement be the norm this century? With every passing AI headline, even the futurists among us are increasingly shaken. In what reads like the Book of Revelation, we've been forewarned of an impending robot Armageddon. Make no mistake, there is enough in the air that reeks of a slowly percolating paradigm shift. In fact, don't be so hard on thee as the trepidation, that's going around like some super-bug, follows.