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.

Finally, we turn our attention to optimal treatment regimes and policy learning. We introduce the concept of the minimum resource threshold policy (MRTP), which is defined as the minimum amount of treatment required to achieve a policy target. Our focus narrows down to estimating the MRTP of the water, sanitation, and hygiene facilities in Senegal to achieve a desirable level of a public health indicator as guided by international organizations.

Bio: I am a postdoctoral researcher in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania, mentored by Prof. Eric J. Tchetgen. In May 2022, I received my Ph.D. in Statistics from the University of Wisconsin-Madison where I was advised by Prof. Hyunseung Kang. Prior to joining the Ph.D. program in July 2017, I worked as a statistician for two and a half years at the Central Bank of Korea. I received a B.S. in Statistics from Seoul National University.
My research broadly focuses on (a) causal inference under interference and non-i.i.d. settings, (b) causal inference under unmeasured confounding, and (c) optimal treatment regimes and policy learning. A common theme in my research is to use non/semiparametric theory and optimization methods to develop efficient and robust estimators of causal quantities in (a)-(c).