Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We investigate how successfully machine-learning (ML) prediction algorithms can be used to estimate causal treatment effects in electricity demand applications with nonexperimental data. We use three prediction algorithms—XGBoost, random forests, and LASSO—to generate counterfactuals using observational data. Using those counterfactuals, we estimate nonexperimental treatment effects and compare them to experimental treatment effects from a randomized experiment for electricity customers who faced critical-peak pricing and information treatments. Our results show that nonexperimental treatment effects based on each algorithm replicate the true treatment effects, even when only using data from treated households. Additionally, when using both treatment households and nonexperimental comparison households, standard two-way fixed effects regressions replicate the experimental benchmark, suggesting little benefit from ML approaches over standard program evaluation methods in that setting.