Breakthrough/Cutting Edge Statistical Methods I intend to deliver a short (1 hour) presentation to some interns and other staff (all of whom are quite statistically savvy, but are not statisticians -- mostly epidemiologists), on cutting edge statistical methods (especially if they are related to biostatistics, epidemiology/public health).  I am hoping to provide a brief, non-technical overview of "cutting edge" methods.  I am seeking any suggestions on topics that you think might be important to mention in this presentation.  I'm looking for methods that may be quite familiar to statisticians, yet may not be to some graduate students with an epidemiology background for example.  "Cutting edge" is also being loosely used here, since I intend to provide a brief overview of things like bootstrap and bootstrap aggregating techniques for variable selection and bootstrapping has been around for decades. It's just so important and might be familiar to these students, so I figured I'd include it.  I also intend to present on propensity score analysis, a few machine learning techniques, etc. 
So my question is, if you had to deliver a similar presentation, what other methods/techniques might you present given the audience and time constraint of 1 hour (my last slide might be a simple list of other important techniques we don't have time to cover, but that might be of interest to the students).
 A: Random causal forests paper by Athey and Wager is neat. ML for causal inference and heterogeneous treatment effects are probably of interest to epidemiologists.
Abstract:

Many scientific and engineering challenges---ranging from personalized
  medicine to customized marketing recommendations---require an
  understanding of treatment effect heterogeneity. In this paper, we
  develop a non-parametric causal forest for estimating heterogeneous
  treatment effects that extends Breiman's widely used random forest
  algorithm. Given a potential outcomes framework with unconfoundedness,
  we show that causal forests are pointwise consistent for the true
  treatment effect, and have an asymptotically Gaussian and centered
  sampling distribution. We also discuss a practical method for
  constructing asymptotic confidence intervals for the true treatment
  effect that are centered at the causal forest estimates. Our
  theoretical results rely on a generic Gaussian theory for a large
  family of random forest algorithms, to our knowledge, this is the
  first set of results that allows any type of random forest, including
  classification and regression forests, to be used for provably valid
  statistical inference. In experiments, we find causal forests to be
  substantially more powerful than classical methods based on
  nearest-neighbor matching, especially as the number of covariates
  increases.

