  While machine learning (ML) methods have received a lot of attention in
recent years, these methods are primarily for prediction. Empirical researchers
conducting policy evaluations are, on the other hand, pre-occupied with causal
problems, trying to answer counterfactual questions: what would have happened
in the absence of a policy? Because these counterfactuals can never be directly
observed (described as the "fundamental problem of causal inference")
prediction tools from the ML literature cannot be readily used for causal
inference. In the last decade, major innovations have taken place incorporating
supervised ML tools into estimators for causal parameters such as the average
treatment effect (ATE). This holds the promise of attenuating model
misspecification issues, and increasing of transparency in model selection. One
particularly mature strand of the literature include approaches that
incorporate supervised ML approaches in the estimation of the ATE of a binary
treatment, under the \textit{unconfoundedness} and positivity assumptions (also
known as exchangeability and overlap assumptions).
  This article reviews popular supervised machine learning algorithms,
including the Super Learner. Then, some specific uses of machine learning for
treatment effect estimation are introduced and illustrated, namely (1) to
create balance among treated and control groups, (2) to estimate so-called
nuisance models (e.g. the propensity score, or conditional expectations of the
outcome) in semi-parametric estimators that target causal parameters (e.g.
targeted maximum likelihood estimation or the double ML estimator), and (3) the
use of machine learning for variable selection in situations with a high number
of covariates.

Machine Learning;Machine Learning