Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables
This addresses the challenge of causal reasoning for AI systems, though it is incremental as it builds on existing IV methods by extending them to nonseparable models.
The paper tackles the problem of counterfactual inference in nonseparable outcome models by using instrumental variables, showing that under specific assumptions, the treatment-outcome relationship becomes uniquely identifiable, and they implement this with a normalizing flow method called Flow IV to accurately recover the outcome function.
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 paper, we make progress 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 non-separable structural models at the population level, existing approaches to counterfactual prediction typically assume additive noise in the outcome. In this paper, we show that under standard IV assumptions, along with the assumptions that latent noises in treatment and outcome are strictly monotonic and jointly Gaussian, 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.