Affinity Groups Speed Networking Social
This session welcomes everyone in the AISTATS community who identifies with one or more affinity groups or as an ally. The event will be in the form of speed networking, where you will meet fellow participants in short 1:1 sessions. Share your experiences, opportunities and challenges with others. At the end of the event, maybe you will find a mentor, mentee, peer or even a collaborator? The event is hosted by Miriam Rateike (Max-Planck-Institute for Intelligent Systems, Tübingen & University of Saarland) and Deborah Dormah Kanubala (University of Saarland).
Visit the Website for Mentorship Engagement here
The goal of these mentoring sessions is to facilitate sharing of experiences between members of the community which would not happen otherwise. Mentoring sessions are hosted on MeMentor and take place at various times from Monday, March 28th to Wednesday, March 30th; see here for exact times and mentor names.
Opening Remarks
New Methods for Taming Endogeneity Bias in Observational Studies: Proxies, Bespoke Instruments and Invalid Instruments
This talk will discuss recent innovations in causal inference literature on the identification and estimation of causal effects from observational data in presence of endogeneity or equivalently unmeasured confounding bias. We will focus primarily on three recent developments: (i) The Proximal causal inference framework that leverages imperfect proxies of unmeasured confounders to remove hidden bias in observational analyses; (ii) Bespoke Instrumental variable framework that leverages a reference population in which a known intervention sets the treatment to generate a bespoke instrument tailored to account for endogeneity in a target population of interest; (iii) Invalid instrumental variable framework that leverages one or more invalid instruments to nevertheless correct for endogeneity bias without requiring that core instrumental variable assumptions hold. We view these new techniques as important strategies to relax the standard un-confoundedness assumptions commonly used in practice. Machine Learning tools implementing the methods and corresponding small bias guarantees will be described along with several empirical examples demonstrating the new methods in action.
