Epidemiologist Donna Spiegelman sez: SUTVA is “mostly not necessary for valid causal estimation and inference most of the time”

Donna Spiegelman shares this presentation she gave at the recent American Causal Inference Conference. I like what she has to say. Here are the two parts of the stable treatment value assumption: 1. No interference between units. As Spiegelman says, nowadays it’s not hard to model spillovers. As I say, untangling spillovers is an ill-posed inverse problem that can be solved using Bayesian inference with reasonable priors. Serious practical work has moved past the demonstrate-that-spillover-doesn’t-matter stage to the just-model-the-spillover-directly stage. 2. Deterministic potential outcomes. As Spiegelman says, in the real world, outcomes are stochastic. Jonas and I talk about this in our Russian roulette paper. The part that I’m less sure about is Spiegelman’s claim that adjustments for pre-treatment variables usually don’t matter. I’m persuaded that they usually don’t matter in the epidemiology and biostatistics applications she’s worked on, but I think that in social science,…