Estimating Causal Effects with Double Machine Learning -- A Method
  Evaluation
Jonathan Fuhr Philipp Berens Dominik Papies
The estimation of causal effects with observational data continues to be a
very active research area. In recent years, researchers have developed new
frameworks which use machine learning to relax classical assumptions necessary
for the estimation of causal effects. In this paper, we review one of the most
prominent methods - "double/debiased machine learning" (DML) - and empirically
evaluate it by comparing its performance on simulated data relative to more
traditional statistical methods, before applying it to real-world data. Our
findings indicate that the application of a suitably flexible machine learning
algorithm within DML improves the adjustment for various nonlinear confounding
relationships. This advantage enables a departure from traditional functional
form assumptions typically necessary in causal effect estimation. However, we
demonstrate that the method continues to critically depend on standard
assumptions about causal structure and identification. When estimating the
effects of air pollution on housing prices in our application, we find that DML
estimates are consistently larger than estimates of less flexible methods. From
our overall results, we provide actionable recommendations for specific choices
researchers must make when applying DML in practice.