Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions

arXiv:2508.04478 · cs.LG · Submitted 2025-08-06 · Read on arXiv

cs.LG

Submitted: 2025-08-06

Updated: 2025-10-21

Terminology

• Energy Efficiency — 55× in this paper · explained in 3 episodes
• Average Treatment Effect — 21× in this paper
• Causal Inference — 19× in this paper · explained in 4 episodes
• Selection Bias — 11× in this paper · explained in 4 episodes
• Conditional Average Treatment Effect — 7× in this paper
• Adaptation — 5× in this paper · explained in 1 episode
• Core — 5× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Archetype — 3× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Causal Discovery — 2× in this paper · explained in 2 episodes
• Data Fusion — 2× in this paper · explained in 3 episodes
• Exchangeability — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Modularity — 2× in this paper · explained in 9 episodes
• National Energy Efficiency Data — 2× in this paper
• Adjustment Sets — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Energy Company Obligation — 1× in this paper
• Foundation Models — 1× in this paper · explained in 7 episodes
• Variable Elimination — 1× in this paper

Sources

• DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

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