Grecov Priscila, Prasanna Ankitha Nandipura, Ackermann Klaus, Campbell Sam, Scott Debbie, Lubman Dan I, Bergmeir Christoph
IEEE Trans Neural Netw Learn Syst. 2024 Apr;35(4):4999-5013. doi: 10.1109/TNNLS.2022.3190984. Epub 2024 Apr 4.
We introduce a novel method to estimate the causal effects of an intervention over multiple treated units by combining the techniques of probabilistic forecasting with global forecasting methods using deep learning (DL) models. Considering the counterfactual and synthetic approach for policy evaluation, we recast the causal effect estimation problem as a counterfactual prediction outcome of the treated units in the absence of the treatment. Nevertheless, in contrast to estimating only the counterfactual time series outcome, our work differs from conventional methods by proposing to estimate the counterfactual time series probability distribution based on the past preintervention set of treated and untreated time series. We rely on time series properties and forecasting methods, with shared parameters, applied to stacked univariate time series for causal identification. This article presents DeepProbCP, a framework for producing accurate quantile probabilistic forecasts for the counterfactual outcome, based on training a global autoregressive recurrent neural network model with conditional quantile functions on a large set of related time series. The output of the proposed method is the counterfactual outcome as the spline-based representation of the counterfactual distribution. We demonstrate how this probabilistic methodology added to the global DL technique to forecast the counterfactual trend and distribution outcomes overcomes many challenges faced by the baseline approaches to the policy evaluation problem. Oftentimes, some target interventions affect only the tails or the variance of the treated units' distribution rather than the mean or median, which is usual for skewed or heavy-tailed distributions. Under this scenario, the classical causal effect models based on counterfactual predictions are not capable of accurately capturing or even seeing policy effects. By means of empirical evaluations of synthetic and real-world datasets, we show that our framework delivers more accurate forecasts than the state-of-the-art models, depicting, in which quantiles, the intervention most affected the treated units, unlike the conventional counterfactual inference methods based on nonprobabilistic approaches.
我们引入了一种新颖的方法，通过将概率预测技术与使用深度学习（DL）模型的全局预测方法相结合，来估计对多个受治疗单元进行干预的因果效应。考虑到用于政策评估的反事实和合成方法，我们将因果效应估计问题重新表述为在没有治疗的情况下受治疗单元的反事实预测结果。然而，与仅估计反事实时间序列结果不同，我们的工作与传统方法的区别在于，我们提议基于过去干预前的受治疗和未受治疗时间序列集来估计反事实时间序列概率分布。我们依赖于时间序列属性和预测方法，并将共享参数应用于堆叠的单变量时间序列以进行因果识别。本文介绍了DeepProbCP，这是一个基于在大量相关时间序列上训练具有条件分位数函数的全局自回归递归神经网络模型，为反事实结果生成准确的分位数概率预测的框架。所提出方法的输出是作为反事实分布的基于样条的表示的反事实结果。我们展示了这种概率方法如何与全局DL技术相结合，以预测反事实趋势和分布结果，从而克服了政策评估问题的基线方法所面临的许多挑战。通常，一些目标干预仅影响受治疗单元分布的尾部或方差，而不是均值或中位数，这在偏态或重尾分布中很常见。在这种情况下，基于反事实预测的经典因果效应模型无法准确捕捉甚至看到政策效果。通过对合成和真实世界数据集的实证评估，我们表明我们的框架比现有模型提供更准确的预测，描绘了干预最影响受治疗单元的分位数，这与基于非概率方法的传统反事实推理方法不同。
