Jianxuan Liu
Department of Statistics
University of South Carolina
Abstract:
The problem of estimating average treatment effect is
of fundamental importance when evaluating the
effectiveness of medical treatments or social intervention policies.
Most of the
existing methods for estimating average treatment effect rely on some parametric
assumptions on
the propensity score model or outcome regression model one way or the other.
In reality, both models are prone to misspecification, which can have undue influence on the estimated average
treatment effect. We propose a new robust approach to estimating
the average treatment effect based on observational data in the challenging situation when neither a plausible parametric
outcome model nor a reliable parametric propensity score model is available. Our estimator can
be considered as a robust extension of the popular class of propensity score weighted estimators.
The new approach has the advantage of being robust, flexible, data adaptive and it can handle
many covariates simultaneously. Adapting a dimension reduction approach, we estimate the
propensity score weights semiparametrically by using a nonparametric link function to relate the
treatment assignment indicator to a low-dimensional structure of the covariates which are formed
typically by several linear combinations of the covariates. We develop a class of consistent
estimators for the average treatment effect and study their theoretical properties. We
demonstrate the robust performance of the new estimators on simulated data and a real data
example of analyzing the effect of maternal smoking on babies' birth weight.
Bio:
Jianxuan Liu is completing a Ph.D. in Statistics at the University of South Carolina.
Her research interests include analysis of high dimensional data, measurement error models, causal inference,
machine learning, and survival analysis.
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