San
Francisco Chapter of the American Statistical Association Seminar
Speaker: |
|
Title: |
Understanding Distributional and Heterogeneous
Causal Effects in Observational Studies with the Instrumental Propensity
Score |
Date: |
Jan 27th from 4:30-6pm (4:30-5pm
social and 5-6pm presentation) |
Location: |
M112
class room at Stanford Medical school It is in the medical center,
Alway building, ground floor, next to the Dean's courtyard. |
Map: |
lane.stanford.edu/graphics/maps/learningspaces_map.pdf Parking on campus after 4pm is free |
Abstract: Confounding has been a main
concern in evaluating treatment effects in observational studies when
randomization of the treatment assignment is not feasible. To address issues
with measured and unmeasured confounding in observational studies, this paper
develops a unified approach to using an instrumental variable (IV) in more
flexible ways to evaluate treatment effects. The approach is based on an
instrumental propensity score conditional on baseline variables, which can then
be incorporated in matching, regression, subclassification or weighting along
with various parametric, semiparametric or nonparametric methods for the
assessment of treatment effects when the standard two-stage linear models do
not work well. Several properties of the instrumental propensity score will be
discussed in the talk. The approach will then be illustrated using
subclassification along with a semiparametric density ratio model (Anderson 1979)
and empirical likelihood (Owen, 1988; Qin and Lawless, 1994). This method
allows us to evaluate distributional and heterogeneous treatment effects in
addition to the overall average treatment effect. Simulation studies show that
the method works well. We apply our method to a study of the effects of
attending a Catholic school versus a public school on subsequent wages using
data from the Wisconsin Longitudinal Study (WLS; Hauser and Sewell,
2005). Some interesting findings on distributional and heterogeneous
effects will be discussed in the talk.