Factor Informed Double Deep Learning For Average Treatment Effect Estimation
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用于平均处理效应估计的因子辅助双重深度学习
- 研究问题
- 当协变量高维、强相关，并以稀疏非线性方式影响倾向得分和结果模型时，如何灵活估计平均处理效应并获得效率保证？
- 方法与思路
- 作者用因子增强神经网络 FAST-NN 分别学习结果函数与倾向得分，再代入增广逆概率加权框架，构造 FIDDLE 估计量。
- 材料陈述的贡献
- 摘要称方法可选择相关变量并学习低维函数结构，在模型误设下保持 ATE 一致性，且在灵活模型族中达到半参数效率。
- 阅读关注点
- 应核对因果识别条件、因子与稀疏非线性结构、干扰函数收敛要求，以及模型误设下的一致性和效率结论的确切范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明识别及速率条件，不能把模型误设下的一致性理解为任意误设均有效。
原文摘要
We investigate the problem of estimating the average treatment effect (ATE) under a very general setup where the covariates can be high-dimensional, highly correlated, and can have sparse nonlinear effects on the propensity and outcome models. We present the use of a Double Deep Learning strategy for estimation, which involves combining recently developed factor-augmented deep learning-based estimators, FAST-NN, for both the response functions and propensity scores to achieve our goal. By using FAST-NN, our method can select variables that contribute to propensity and outcome models in a completely nonparametric and algorithmic manner and adaptively learn low-dimensional function structures through neural networks. Our proposed novel estimator, FIDDLE (Factor Informed Double Deep Learning Estimator), estimates ATE based on the framework of augmented inverse propensity weighting AIPW with the FAST-NN-based response and propensity estimates. FIDDLE consistently estimates ATE even under model misspecification and is flexible to also allow for low-dimensional covariates. Our method achieves semiparametric efficiency under a very flexible family of propensity and outcome models. We present extensive numerical studies on synthetic and real datasets to support our theoretical guarantees and establish the advantages of our methods over other traditional choices, especially when the data dimension is large.