High-dimensional propensity score in adjusting for unmeasured confounders

High-dimensional propensity score in adjusting for unmeasured confounders

Overview

Description

In order to examine the claim that the high-dimensional propensity score algorithm can adjust for unmeasured confounding, we hide information from the algorithm to examine its ability to compensate by selecting proxies of what was hidden. Performance of the algorithm within the hidden information context is then compared to its performance within the full information context.

Manuscripts

Presentations

Project Team