When are Causal Inference Methods Needed to Answer Causal Questions?

Earlier this year we discussed Donna Spiegelman’s talk, “Rethinking SUTVA and Causal Identification: An Epidemiologist’s Perspective,” which she summarized as follows:

She’s giving a followup talk online on Mon 12 Oct, 3:30pm. Here’s the abstract:

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.

The talk will be followed by a discussion. Click on the link to register (or show up in person if you’re at Yale).