Identifying effects of multiple treatments in the presence of unmeasured confounding

Understand

Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences.

• The problem of unmeasured confounding in settings with multiple treatments is most common in statistical genetics and bioinformatics settings, where researchers have developed many successful statistical strategies without engaging deeply with the causal aspects of the problem.
• Recently there have been a number of attempts to bridge the gap between these statistical approaches and causal inference, but these attempts have either been shown to be flawed or have relied on fully parametric assumptions.
• In this paper, we propose two strategies for identifying and estimating causal effects of multiple treatments in the presence of unmeasured confounding.