用变量流模型解决非可分结果中的反事实推断问题
Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables
- 基于工具变量与可逆函数结构，从观测数据中识别处理-结果关系
- 提出基于归一化流的估计方法，在真实数据集上实现高精度反事实预测
- 适合需要因果推断的决策系统开发人员，如医疗、金融领域
为实现人类水平智能，学习算法需融入因果推理。但因果识别，尤其是反事实推理仍具挑战。本文通过工具变量（IV）推进非可分结果模型中的反事实推断。尽管已有研究将IV用于非可分结果的效应估计，但现有反事实预测方法通常假设结果为一维且具有加性噪声。本文证明，在标准IV假设下，若结果函数可逆且具三角结构，则处理-结果关系可由观测数据识别。我们进一步提出利用归一化流学习结果函数的方法，该估计器可用于反事实推断，称作Flow IV。
原文摘要 · Abstract (English)
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. While IV methods for effect estimation have been extended to nonseparable outcome models under different assumptions, existing IV approaches to counterfactual prediction typically assume one-dimensional outcomes and additive noise. In this paper, we show that under standard IV assumptions, along with the assumption that the outcome function is invertible and has a triangular structure, then the treatment-outcome relationship becomes identifiable from observed data. We furthermore propose a method to learn the outcome function utilizing normalizing flows. This outcome function estimator can then be used to perform counterfactual inference. We refer to the method as Flow IV.
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