Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes
Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes
复制标题
异质家庭对分时电价定价方案反应的因果树估计
DOI：
10.17863/cam.33793
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
M. Weeks
中科院分区：
文献类型：
--
作者：
Eoghan O'Neill;M. Weeks
我们研究了引入分时计价(TOU)定价方案的分配效应，在该方案中，每千瓦时的电价取决于消耗时间。这些定价方案是由智能电表实现的，智能电表可以定期(即每半小时)记录用电量。使用因果树和被称为因果森林的因果树估计聚合(Athee&Imbens 2016，Wager&Ahe 2017)，我们考虑了分时电价方案对家庭电力需求的影响与引入新定价方案之前可观察到的一系列变量之间的关联。因果树提供了异质性的可解释描述，而因果森林可用于获得治疗效果的个体特定估计。鉴于政策制定者通常对某一给定预测背后的因素感兴趣，因此有必要深入了解这一大集合中的哪些变量是最常被选择的。一个关键的挑战来自于这样一个事实，即基于树的方法产生的分区对次抽样很敏感，而使用诸如因果森林等集合方法产生的估计更稳定，但更难解释。为了解决这个问题，我们使用变量重要性度量来考虑因果森林算法最常选择的变量。考虑到许多标准变量重要性度量可能偏向于连续变量，我们通过在变量重要性结果中包含基于排列的测试来解决这个问题。
We examine the distributional effects of the introduction of Time-of-Use (TOU) pricing schemes where the price per kWh of electricity usage depends on the time of consumption. These pricing schemes are enabled by smart meters, which can regularly (i.e. half-hourly) record consumption. Using causal trees, and an aggregation of causal tree estimates known as a causal forest (Athey & Imbens 2016, Wager & Athey 2017), we consider the association between the effect of TOU pricing schemes on household electricity demand and a range of variables that are observable before the introduction of the new pricing schemes. Causal trees provide an interpretable description of heterogeneity, while causal forests can be used to obtain individual-specific estimates of treatment effects. Given that policy makers are often interested in the factors underlying a given prediction, it is desirable to gain some insight to which variables in this large set are most often selected. A key challenge follows from that fact that partitions generated by tree-based methods are sensitive to subsampling, while the use of ensemble methods such as causal forests produce more stable, but less interpretable estimates. To address this problem we utilise variable importance measures to consider which variables are chosen most often by the causal forest algorithm. Given that a number of standard variable importance measures can be biased towards continuous variables, we address this issue by including permutation-based tests for our variable importance results.
影响因子：
2.7
作者：
Nie, X.;Wager, S.
通讯作者：
Wager, S.