Abstract Number:
3105
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Animesh Mitra (1), Bikram Karmakar (1), Michael Daniels (1)
Institutions:
(1) University of Florida, Gainesville
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
In observational studies, no unmeasured confounding (the ignorability of the treatment assignment) is typically assumed to identify the causal effect. However, this assumption is untestable and often fails to hold in practice. Recent work has shown that when a resistant population is available, the conditional average treatment effect on the treated can still be identified without assuming ignorability of the treatment assignment. This estimand Resistant Population Calibration Of Variance (RPCOVA), however requires estimation of the conditional variance function unlike other estimands including inverse probability weighting, differences in the conditional expectations, and the doubly robust estimands. We propose a nonparametric Bayesian approach for inference on this estimand using a dependent Dirichlet process to model the response. We establish weak consistency of the estimator and explore its finite sample performance in simulations.
Keywords:
Causal Inference|Unmeasured Confounders|Gibbs Sampler|Non Parametric Bayesian|Conditional Average Treatment Effect on the Treated|
Sponsors:
Section on Bayesian Statistical Science
Tracks:
Bayesian nonparametrics
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