WP-032: Fiona Burlig, Christopher Knittel, David Rapson, Mar Reguant, and Catherine Wolfram, "Machine Learning from Schools about Energy Efficiency" (Revised January 2019)

We implement a machine learning approach for estimating treatment effects using high-frequency panel data to study the effectiveness of energy efficiency in K-12 schools in California. We find that energy efficiency upgrades deliver only 70 percent of ex ante expected savings on average. We find that the estimates using a standard panel fixed effects approach imply smaller savings and are more sensitive to specification and outliers. Our findings highlight the potential benefits of using machine learning in applied settings and align with a growing literature documenting a gap between expected and realized energy efficiency savings.