We evaluate the causal effect of hour-ahead price interventions on the reduction of residential electricity consumption, using a large-scale experiment on 7,000 households in California. In addition to this experimental approach, we also develop a non-experimental framework that allows for an estimation of the desired treatment effect on an individual level by estimating user-level counterfactuals using time series prediction. This approach crucially eliminates the need for a randomized experiment. Both approaches estimate a reduction of 0.10 kWh (11%) per Demand Response event and household. We also analyze an adaptive targeting scheme, which assigns customized interventions to users based on their histories to increase the reduction-per-payout ratio by 107%.