International Day Of Women in Statistics and Data Science

Causal inference is a term that has become a buzzword and a popular field of research in many fields recently, and arises often in statistics. Causal methodologies can sound overly complex to many collaborators, and others may fixate on the need to have causal results from all possible studies. New researchers and scientists are left with questions of how often to employ these newer methods and frameworks of thinking, when it is appropriate to do so, and what results can actually be drawn from causal analyses.
In this session, we will hear from four professionals who use causal inference in their day-to-day practice. First, Dr. Candice Johnson will discuss whether certain causal studies are inherently gendered by examining hazards in the workforce in male-dominated fields. Dr. Lucy McGowan will talk about whether interaction effects are feasible, clinically significant, and better than subgroup-specific effects in real-world trials. Dr. Maria DeYoreo will explore inferential approaches to estimate causal effects from a study on the effect of state-funded family planning policies on birth outcomes. Finally, Dr. Lu Wang will discuss applying a causal framework to personalized healthcare, demonstrating how causal inference can support flexible, patient-centered decision-making in routine clinical practice.