Title: Estimating Average Treatment Effects with a Double-Index Propensity Score,
Abstract: We consider estimating average treatment effects (ATE) of a binary treatment
in observational data when data-driven variable selection is needed to select
relevant covariates from a moderately large number of available covariates
$\mathbf{X}$. To leverage covariates among $\mathbf{X}$ predictive of the
outcome for efficiency gain while using regularization to fit a parameteric
propensity score (PS) model, we consider a dimension reduction of $\mathbf{X}$
based on fitting both working PS and outcome models using adaptive LASSO. A
novel PS estimator, the Double-index Propensity Score (DiPS), is proposed, in
which the treatment status is smoothed over the linear predictors for
$\mathbf{X}$ from both the initial working models. The ATE is estimated by
using the DiPS in a normalized inverse probability weighting (IPW) estimator,
which is found to maintain double-robustness and also local semiparametric
efficiency with a fixed number of covariates $p$. Under misspecification of
working models, the smoothing step leads to gains in efficiency and robustness
over traditional doubly-robust estimators. These results are extended to the
case where $p$ diverges with sample size and working models are sparse.
Simulations show the benefits of the approach in finite samples. We illustrate
the method by estimating the ATE of statins on colorectal cancer risk in an
electronic medical record (EMR) study and the effect of smoking on C-reactive
protein (CRP) in the Framingham Offspring Study.