AI RESEARCH
Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables
arXiv CS.LG
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ArXi:2508.01321v2 Announce Type: replace-cross To reach human level intelligence, learning algorithms need to incorporate causal reasoning. But identifying causality, and particularly counterfactual reasoning, remains elusive. In this paper, we make progress on counterfactual inference in nonseparable outcome models by utilizing instrumental variables (IVs). IVs are a classic tool for mitigating bias from unobserved confounders when estimating causal effects.