Causal Inference for Complex Real-world Questions | Chan Park
Abstract: Causal inference has gained popularity due to its ability to draw causal conclusions for real-world questions. While much of the existing work in causal inference revolves around determining average treatment effects under no interference and no unmeasured confounding assumptions, these approaches may not be suitable for answering causal questions in complex settings. In the talk, we focus on the following three topics, each accompanied by relevant applications.
First, we discuss causal inference under interference and non-i.i.d. settings. Interference refers to a phenomenon where one’s outcome is affected by others’ treatment status; for instance, one’s own risk of flu infection is affected by family members’ flu vaccination status. We explore two estimators, a point-estimator and a bound-estimator, that are developed to infer causal effects in the presence of interference.
Second, we explore causal inference under the presence of unmeasured confounders, variables that affect both treatment status and outcome. We demonstrate that ignoring the potential existence of unmeasured confounding can lead to incorrect causal conclusions using data on the Zika virus outbreak. Recently developed frameworks, including universal difference-in-differences and single proxy control, are discussed to address the challenges posed by unmeasured confounding.