When are Causal Inference Methods Needed to Answer Causal Questions? Panel Discussion to Follow.

Donna Spiegelman, ScD | Yale School of Public Health

When do specialized causal inference methods add value beyond standard approaches? Drawing from her unique perspective as both an epidemiologist and a biostatistician, Donna Spiegelman will consider the circumstances under which commonly invoked causal assumptions are necessary for causal inferences to be validly made from data, concluding that often not. She will show that valid learning can occur 1) under conditions much less restrictive than required by current widely used methods, 2) when real world implementation of interventions vary, and 3) when interventions spill over to others not directly exposed, thereby obviating components of the SUTVA assumption. She will provide evidence that measurement error is the major source of bias in observational research, not confounding, whose bias is rather tightly bounded. Finally, she will discuss the eternal challenge in science: after exhaustive efforts to collect data to predict important outcomes, a substantial proportion of the variation in occurrences of these outcomes appear to be entirely random.

A discussion will follow, featuring formal remarks by Lee Kennedy-Shaffer and contributions from Bhramar Mukherjee and others to examine and debate the ideas raised in the lecture.