Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In spite of their popularity, no scalable non-parametric estimator exist for marginal structural models with multi-valued and time-varying treatments. In this paper, we use machine learning together with recent developments in semiparametric efficiency theory for longitudinal studies to propose such an estimator. The proposed estimator is based on a study of the non-parametric identifying functional, including first order von-Mises expansions as well as the efficient influence function and the efficiency bound. We show conditions under which the proposed estimator is efficient, asymptotically normal, and sequentially doubly robust in the sense that it is consistent if, for each time point, either the outcome or the treatment mechanism is consistently estimated. We perform a simulation study to illustrate the properties of the estimators, and present the results of our motivating study on a COVID-19 dataset studying the impact of mobility on the cumulative number of observed cases.

We present an inverse probability weighted estimator for survival analysis under informative right censoring. Our estimator has the novel property that it converges to a normal variable at $n^{1/2}$ rate for a large class of censoring probability estimators, including many data-adaptive (e.g., machine learning) prediction methods. We present the formula of the asymptotic variance of the estimator, which allows the computation of asymptotically correct confidence intervals and p-values under data-adaptive estimation of the censoring and treatment probabilities. We demonstrate the asymptotic properties of the estimator in simulation studies, and illustrate its use in a phase III clinical trial for estimating the effect of a novel therapy for the treatment of breast cancer.