We examine the household-specific effects of the introduction of Time-of-Use (TOU) electricity pricing schemes. Using a causal forest (Athey and Imbens, 2016; Wager and Athey, 2018; Athey et al., 2019), we consider the association between past consumption and survey variables, and the effect of TOU pricing on household electricity demand. We describe the heterogeneity in household variables across quartiles of estimated demand response and utilise variable importance measures.
Household-specific estimates produced by a causal forest exhibit reasonable associations with covariates. For example, households that are younger, more educated, and that consume more electricity, are predicted to respond more to a new pricing scheme. In addition, variable importance measures suggest that some aspects of past consumption information may be more useful than survey information in producing these estimates.

Bayesian Additive Regression Trees (BART) (Chipman et al. 2010) and Bayesian Causal Forests (BCF) (Hahn et al. 2017) are state-of-the-art machine learning algorithms for prediction and treatment effect estimation. These methods involve averaging predictions from sum-of-tree models, typically drawn using Monte Carlo Markov Chain methods.

This paper introduces conceptually and computationally simple alternatives to MCMC implementations of BART, which can exhibit comparable performance.

An importance sampling based implementation of BART (BART-IS) builds on the ideas of Hernandez et al. (2018) and Quadrianto & Ghahramani (2014). Unlike most BART implementations, BART-IS has a data independent prior. This paper also contains an extension to treatment effect estimation, BCF-IS.

In addition, I describe Bayesian Causal Forests using Bayesian Model Averaging (BCF-BMA), an implementation of BCF (Hahn et al. 2017) that extends an improved implementation of BART-BMA Hernandez et al. (2018) to treatment effect estimation.