Seminar Series: Eben Kenah

Headshot of Eben Kenah, the speaker of this event.
September 5, 2024
3:00PM - 4:00PM
EA170

Date Range
2024-09-05 15:00:00 2024-09-05 16:00:00 Seminar Series: Eben Kenah Speaker: Eben Kenah, Biostatistics, College of Public Health, OSUTitle: Potential Outcomes Approaches to Selection BiasAbstract: Neither the structural approach to selection bias nor the traditional definition of selection bias can be stated explicitly in terms of the potential outcomes used to define causal effects. We propose a novel definition of selection bias in terms of potential outcomes that captures all selection bias under the structural approach and under the traditional definition of selection bias. It is nonparametric, and it can be analyzed using single-world intervention graphs (SWIGs) both under and away from the null hypothesis. It allows the simultaneous analysis of confounding and selection bias, explicitly links the selection of study participants to the estimation of causal effects, and can be adapted to handle selection bias in descriptive epidemiology. It provides a novel perspective on the mechanisms that can generate selection bias, and it simplifies the analysis of matched studies and case-cohort studies. Finally, I discuss a simplification and extension of this approach being developed by Patrick Schnell that uses a type of causal graph that we call partial SWIGs. EA170 Department of Statistics stat@osu.edu America/New_York public

Speaker: Eben Kenah, Biostatistics, College of Public Health, OSU

Title: Potential Outcomes Approaches to Selection Bias

Abstract: Neither the structural approach to selection bias nor the traditional definition of selection bias can be stated explicitly in terms of the potential outcomes used to define causal effects. We propose a novel definition of selection bias in terms of potential outcomes that captures all selection bias under the structural approach and under the traditional definition of selection bias. It is nonparametric, and it can be analyzed using single-world intervention graphs (SWIGs) both under and away from the null hypothesis. It allows the simultaneous analysis of confounding and selection bias, explicitly links the selection of study participants to the estimation of causal effects, and can be adapted to handle selection bias in descriptive epidemiology. It provides a novel perspective on the mechanisms that can generate selection bias, and it simplifies the analysis of matched studies and case-cohort studies. Finally, I discuss a simplification and extension of this approach being developed by Patrick Schnell that uses a type of causal graph that we call partial SWIGs.