Reviews: Adapting Neural Networks for the Estimation of Treatment Effects

The paper addresses the problem of inferring causal effects using observational data, under the "no-hidden confounders" scenario. Recently there has been much interest in the problem from the machine learning community, including several papers proposing neural net architectures tailored for this problem. This paper proposes a new regularization scheme for this task. The idea is inspired by TMLE, a well known method for doubly-robust estimation of treatment effects. However, TMLE is only an inspiration - the regularization scheme and resulting architecture are distinct and novel.