  Estimation of the Average Treatment Effect (ATE) is often carried out in 2
steps, wherein the first step, the treatment and outcome are modeled, and in
the second step the predictions are inserted into the ATE estimator. In the
first steps, numerous models can be fit to the treatment and outcome, including
using machine learning algorithms. However, it is a difficult task to choose
among the hyperparameter sets which will result in the best causal effect
estimation and inference. Multiply Robust (MR) estimator allows us to leverage
all the first-step models in a single estimator. We show that MR estimator is
$n^r$ consistent if one of the first-step treatment or outcome models is $n^r$
consistent. We also show that MR is the solution to a broad class of estimating
equations, and is asymptotically normal if one of the treatment models is
$\sqrt{n}$-consistent. The standard error of MR is also calculated which does
not require a knowledge of the true models in the first step. Our simulations
study supports the theoretical findings.
