  We investigate the problem of machine learning-based (ML) predictive
inference on individual treatment effects (ITEs). Previous work has focused
primarily on developing ML-based meta-learners that can provide point estimates
of the conditional average treatment effect (CATE); these are model-agnostic
approaches for combining intermediate nuisance estimates to produce estimates
of CATE. In this paper, we develop conformal meta-learners, a general framework
for issuing predictive intervals for ITEs by applying the standard conformal
prediction (CP) procedure on top of CATE meta-learners. We focus on a broad
class of meta-learners based on two-stage pseudo-outcome regression and develop
a stochastic ordering framework to study their validity. We show that inference
with conformal meta-learners is marginally valid if their (pseudo outcome)
conformity scores stochastically dominate oracle conformity scores evaluated on
the unobserved ITEs. Additionally, we prove that commonly used CATE
meta-learners, such as the doubly-robust learner, satisfy a model- and
distribution-free stochastic (or convex) dominance condition, making their
conformal inferences valid for practically-relevant levels of target coverage.
Whereas existing procedures conduct inference on nuisance parameters (i.e.,
potential outcomes) via weighted CP, conformal meta-learners enable direct
inference on the target parameter (ITE). Numerical experiments show that
conformal meta-learners provide valid intervals with competitive efficiency
while retaining the favorable point estimation properties of CATE
meta-learners.
