B1936
Title: The synthetic instrument
Authors: Dingke Tang - University of Toronto (Canada)
Dehan Kong - University of Toronto (Canada)
Linbo Wang - University of Toronto (Canada)
Linbo Wang - University of Toronto (Canada) [presenting]
Abstract: In many observational studies, researchers are interested in studying the effects of multiple treatments on the same outcome. Unmeasured confounding is a key challenge in these studies as it may bias the causal effect estimate. To mitigate this bias, we introduce a novel device, called synthetic instrument, to leverage the information contained in multiple treatments for causal effect identification and estimation. We show that under linear structural equation models, the problem of causal effect estimation can be formulated as an $\ell_0$ penalization problem, and hence can be solved efficiently using off-the-shelf software. Simulations show that our approach outperforms state-of-art methods in both low-dimensional and high-dimensional settings. We further illustrate our method using a mouse obesity dataset.
