论文标题
具有分类背景变量的深度反事实估计
Deep Counterfactual Estimation with Categorical Background Variables
论文作者
论文摘要
反事实查询通常称为因果推理阶梯的第三个梯级，通常问“如果呢？”回顾性问题。估计反事实的标准方法在于使用准确反映基础数据生成过程的结构方程模型。但是，这种模型在实践中很少可用，并且通常希望仅从观察数据中推断出这些模型。不幸的是，正确的结构方程模型通常无法从观察到的事实分布中识别。然而，在这项工作中，我们表明，在假设治疗反应的主要潜在贡献者是绝对的，仍然可以可靠地预测反事实。在此假设的基础上，我们引入了反事实查询预测（CFQP），这是一种新的方法，可以从反复观察中推断出背景变量分类时的反事实。我们表明，在理论上和经验上，我们的方法在时间序列和图像数据上都显着优于先前可用的基于深度学习的反事实方法。我们的代码可在https://github.com/edebrouwer/cfqp上找到。
Referred to as the third rung of the causal inference ladder, counterfactual queries typically ask the "What if ?" question retrospectively. The standard approach to estimate counterfactuals resides in using a structural equation model that accurately reflects the underlying data generating process. However, such models are seldom available in practice and one usually wishes to infer them from observational data alone. Unfortunately, the correct structural equation model is in general not identifiable from the observed factual distribution. Nevertheless, in this work, we show that under the assumption that the main latent contributors to the treatment responses are categorical, the counterfactuals can be still reliably predicted. Building upon this assumption, we introduce CounterFactual Query Prediction (CFQP), a novel method to infer counterfactuals from continuous observations when the background variables are categorical. We show that our method significantly outperforms previously available deep-learning-based counterfactual methods, both theoretically and empirically on time series and image data. Our code is available at https://github.com/edebrouwer/cfqp.
