STA 290 Seminar Series
DATE: Monday November 20th, 10:30am
LOCATION: MSB 1147, Colloquium Room
SPEAKER: Linbo Wang
TITLE: “Causal Inference with Unmeasured Confounding: an Instrumental Variable Approach”
ABSTRACT: Instrumental variables (IVs) are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard IV model, however, the average treatment effect (ATE) is only partially identifiable. To address this, we propose novel assumptions that allow for identification of the ATE. Our identification assumptions are clearly separated from model assumptions needed for estimation, so that researchers are not required to commit to a specific observed data model in establishing identification. We then construct multiple estimators that are consistent under three different observed data models, and multiply robust estimators that are consistent in the union of these observed data models. We pay special attention to the case of binary outcomes, for which we obtain bounded estimators of the ATE that are guaranteed to lie between -1 and 1.