Estimating Heterogeneous Treatment Effects in Residential Demand Response

TL;DR

This paper assesses the impact of hour-ahead electricity price interventions on residential consumption, using causal inference methods to identify heterogeneity among households and improve targeting strategies.

Contribution

It introduces the use of causal decision trees for detecting treatment effect heterogeneity in residential demand response, outperforming traditional clustering methods.

Findings

Average treatment effect of ~0.10 kWh per intervention

Causal decision trees outperform k-means clustering in detecting heterogeneity

Methods can improve household targeting for cost efficiency

Abstract

We evaluate the causal effect of hour-ahead price interventions on the reduction in residential electricity consumption using a data set from a large-scale experiment on 7,000 households in California. By estimating user-level counterfactuals using time-series prediction, we estimate an average treatment effect of ~0.10 kWh (11%) per intervention and household. Next, we leverage causal decision trees to detect treatment effect heterogeneity across users by incorporating census data. These decision trees depart from classification and regression trees, as we intend to estimate a causal effect between treated and control units rather than perform outcome regression. We compare the performance of causal decision trees with a simpler, yet more inaccurate k-means clustering approach that naively detects heterogeneity in the feature space, confirming the superiority of causal decision trees. Lastly, we comment on how our methods to detect heterogeneity can be used for targeting households to improve cost efficiency.