Seminars Details


Virtual Seminar: A brief introduction to cause-effect estimation in instrumental variable models

Niki Kilbertus, ICB

We will start with a general motivation for cause-effect estimation and describe common challenges such as identifiability. Next, we take a closer look at the instrumental variable setting and how an instrument can aid identification. While most approaches to achieve identifiability require one-size-fits-all assumptions such as an additive error model for the outcome, we will present a framework for partial identification, which provides lower and upper bounds on the causal treatment effect. The approach leverages advances in gradient-based optimization for the non-convex objective and works in the most general case, where instrument, treatment and outcome are continuous. Finally, we demonstrate on a set of synthetic and real-world data that our bounds capture the causal effect when additive methods fail, providing a useful range of answers compatible with observation as opposed to relying on unwarranted structural assumptions.