A fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given some measured confounders. This unconfoundedness assumption is more plausible if a large number of baseline covariates are included in the analysis as we often have no prior knowledge of which variables can be important confounders. Thus, estimation of treatment effects with a large number of covariates has received considerable attention in recent years. Most of the existing methods require specifying certain parametric models involving the outcome, treatment and confounding variables, and employ a variable selection procedure to identify confounders. However, selection of the right set of confounders depends on correct specification of the working models. The bias due to model misspecification and incorrect selection of confounders can yield misleading results. In this talk, I will introduce a new robust and efficient approach for inference about the average treatment effect via a flexible modeling strategy incorporating penalized variable selection. Specifically, we consider an estimator constructed based on an efficient influence function which involves a propensity score function and an outcome regression function. We then propose a new sparse sufficient dimension reduction approach to estimating these two functions, without making restrictive parametric modeling assumptions. The proposed estimator of the average treatment effect is asymptotically normal and semiparametric efficient without the need for variable selection consistency. The proposed methods are illustrated via simulation studies and a biomedical application.