Metalearners for estimating heterogeneous treatment effects using machine learning

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms-such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks-to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in…

• NS
  National Science Foundation

  Awards: CCF-0939370, DMS 1713083, 0939370
• OO
  Office of Naval Research

  Awards: N00014-16-1-2664, N00014-15-1-2367, N00014-17-1-2176, N00014-17-1, N00014
• AR
  Army Research Office

  Awards: W911NF-17-10005, W911NF