STATISTICS Seminars Series: Jose M. Peña

Topic Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables

Abstract To reach human level intelligence, learning algorithms need to incorporate causal reasoning. But identifying causality, and particularly counterfactual reasoning, remains an elusive task. In this talk, we show the progress we have made on this task by utilizing instrumental variables (IVs). IVs are a classic tool for mitigating bias from unobserved confounders when estimating causal effects. While IV methods have been extended to nonseparable structural models at the population level, existing approaches to counterfactual prediction typically assume additive noise in the outcome. In this talk, we show that under

standard IV assumptions, along with the assumptions that latent noises in treatment and outcome are strictly monotonic, the treatment–outcome relationship becomes uniquely identifiable from observed data. This enables counterfactual inference even in nonseparable models. We implement our approach by training a normalizing flow to maximize the likelihood of the observed data, demonstrating accurate recovery of the underlying outcome function. We call our method Flow IV.