We investigate how well machine learning counterfactual prediction tools can estimate causal treatment effects. We use three prediction algorithms—XGBoost, random forests, and LASSO—to estimate treatment effects using observational data. We compare those results to causal effects from a randomized experiment for electricity customers who faced critical-peak pricing and information treatments. Our results show that each algorithm replicates the true treatment effects, even when using data from treated households only. Additionally, when using both treatment households and nonexperimental comparison households, simpler difference-in-differences methods replicate the experimental benchmark, suggesting little benefit from ML approaches over standard program evaluation methods.
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