  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.
