  In this paper, we propose a data-adaptive empirical likelihood-based approach
for treatment effect estimation and inference, which overcomes the obstacle of
the traditional empirical likelihood-based approaches in the high-dimensional
setting by adopting penalized regression and machine learning methods to model
the covariate-outcome relationship. In particular, we show that our procedure
successfully recovers the true variance of Zhang's treatment effect estimator
(Zhang, 2018) by utilizing a data-splitting technique. Our proposed estimator
is proved to be asymptotically normal and semiparametric efficient under mild
regularity conditions. Simulation studies indicate that our estimator is more
efficient than the estimator proposed by Wager et al. (2016) when random
forests are employed to model the covariate-outcome relationship. Moreover,
when multiple machine learning models are imposed, our estimator is at least as
efficient as any regular estimator with a single machine learning model. We
compare our method to existing ones using the ACTG175 data and the GSE118657
data, and confirm the outstanding performance of our approach.
