Causality pursuit from heterogeneous environments via neural adversarial invariance learning
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通过神经对抗不变性学习从异质环境中追寻因果关系
- 研究问题
- 在多个环境的联合分布变化而部分变量的条件期望保持不变时，如何识别不变或准因果变量，并处理具有异质效应的内生变量？
- 方法与思路
- 作者提出 FAIR 极小极大正则化框架，以对抗检验推动预测不变性，结合神经网络、降温 Gumbel 近似和随机梯度下降上升求解。
- 材料陈述的贡献
- 摘要称在识别条件下可找到不变及准因果变量，并适应低维复合结构；在结构因果模型和充分异质性条件下可对应精确因果机制。
- 阅读关注点
- 应核对不变变量、准因果与实际因果的定义，最小识别条件和充分异质性要求，以及非渐近结论与优化算法之间的关系。
- 阅读边界
- 依据 arXiv 预印本摘要，不能据此声称已同行评审或期刊接收，也不能推断刊本变化；识别条件未展开，不变预测不能无条件等同于因果识别。
原文摘要
Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for addressing nonparametric invariance and causality learning in regression models across multiple environments, where the joint distribution of response variables and covariates varies, but the conditional expectations of outcome given an unknown set of quasi-causal variables are invariant. The challenge of finding such an unknown set of quasi-causal or invariant variables is compounded by the presence of endogenous variables that have heterogeneous effects across different environments. The proposed Focused Adversarial Invariant Regularization (FAIR) framework utilizes an innovative minimax optimization approach that drives regression models toward prediction-invariant solutions through adversarial testing. Leveraging the representation power of neural networks, FAIR neural networks (FAIR-NN) are introduced for causality pursuit. It is shown that FAIR-NN can find the invariant variables and quasi-causal variables under a minimal identification condition and that the resulting procedure is adaptive to low-dimensional composition structures in a non-asymptotic analysis. Under a structural causal model, variables identified by FAIR-NN represent pragmatic causality and provably align with exact causal mechanisms under conditions of sufficient heterogeneity. Computationally, FAIR-NN employs a novel Gumbel approximation with decreased temperature and a stochastic gradient descent ascent algorithm. The procedures are demonstrated using simulated and real-data examples.