Can predictive models be used for causal inference?
预测模型能否用于因果推断？AI 解读 ↓
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预测模型能否用于因果推断？
- 研究问题
- 机器学习预测模型在依据因果结构约束特征选择后，能否估计因果效应，并改善共线性结构变化时的泛化表现？
- 方法与思路
- 作者按 Pearl 后门调整准则选择特征，比较机器学习与深度学习模型的效应估计，并考察算法结构和超参数对偏差的影响。
- 材料陈述的贡献
- 摘要称部分算法尤其深度神经网络可在共线性下给出近无偏估计，因果约束特征选择的影响较大，并改善变化环境中的泛化。
- 阅读关注点
- 应核对因果图和调整集合如何获得、效应如何从预测模型提取，以及预测与推断调参目标、共线性变化实验的具体设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未说明完整识别条件和实验范围，近无偏表现不能推广为通用保证。
原文摘要
Supervised machine learning (ML) and deep learning (DL) algorithms excel at predictive tasks, but it is commonly assumed that they often do so by exploiting non-causal correlations, which may limit both interpretability and generalizability. Here, we show that this trade-off between explanation and prediction is not as deep and fundamental as expected. Whereas ML and DL algorithms will indeed tend to use non-causal features for prediction when fed indiscriminately with all data, it is possible to constrain the learning process of any ML and DL algorithm by selecting features according to Pearl's backdoor adjustment criterion. In such a situation, some algorithms, in particular deep neural networks, can provide near unbiased effect estimates under feature collinearity. Remaining biases are explained by the specific algorithmic structures as well as hyperparameter choice. Consequently, optimal hyperparameter settings are different when tuned for prediction or inference, confirming the general expectation of a trade-off between prediction and explanation. However, the effect of this trade-off is small compared to the effect of a causally constrained feature selection. Thus, once the causal relationship between the features is accounted for, the difference between prediction and explanation may be much smaller than commonly assumed. We also show that such causally constrained models generalize better to new data with altered collinearity structures, suggesting generalization failure may often be due to a lack of causal learning. Our results not only provide a perspective for using ML for inference of (causal) effects but also help to improve the generalizability of fitted ML and DL models to new data.