  Many popular methods for building confidence intervals on causal effects
under high-dimensional confounding require strong "ultra-sparsity" assumptions
that may be difficult to validate in practice. To alleviate this difficulty, we
here study a new method for average treatment effect estimation that yields
asymptotically exact confidence intervals assuming that either the conditional
response surface or the conditional probability of treatment allows for an
ultra-sparse representation (but not necessarily both). This guarantee allows
us to provide valid inference for average treatment effect in high dimensions
under considerably more generality than available baselines. In addition, we
showcase that our results are semi-parametrically efficient.
