Learning Representations of Instruments for Partial Identification of
Treatment Effects
Learning Representations of Instruments for Partial Identification of
Treatment Effects
Reliable estimation of treatment effects from observational data is important in many disciplines such as medicine. However, estimation is challenging when unconfoundedness as a standard assumption in the causal inference literature is violated. In this work, we leverage arbitrary (potentially high-dimensional) instruments to estimate bounds on the conditional average treatment …