Department seminar by Samrachana Adhikari, Harvard Medical SchoolExport this event to calendar

Wednesday, November 15, 2017 — 9:00 AM EST

Causal inference in observational data with unmeasured confounding

Observational data introduces many practical challenges for causal inference. In this talk, I will focus on a particular issue when there are unobserved confounders such that the assumption of “ignorability” is violated. For making a causal inference in the presence of unmeasured confounders, instrumental variable (IV) analysis plays a crucial role. I will introduce a hierarchical Bayesian likelihood-based IV analysis under a Latent Index Modeling framework to jointly model outcomes and treatment status, along with necessary assumptions and sensitivity analysis to make a valid causal inference. The innovation in our methodology is an extension of existing parametric approach by i.) accounting for an unobserved heterogeneity via a latent factor structure, and ii.) allowing non-parametric error distributions with Dirichlet process mixture models. We demonstrate utility of our model in comparing effectiveness of two different types of vascular access for a cardio-vascular procedure.

Location 
M3 - Mathematics 3
Room: 3127
200 University Avenue West
Waterloo, ON N2L 3G1
Canada

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