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Unlike parametric regression, machine learning (ML) methods do not generallyrequire precise knowledge of the true data generating mechanisms. As such,numerous authors have advocated for ML methods to estimate causal effects.Unfortunately, ML algorithms can perform worse than parametric regression. Wedemonstrate the performance of ML-based single- and double-robust estimators.We use 100 Monte Carlo samples with sample sizes of 200, 1200, and 5000 toinvestigate bias and confidence interval coverage under several scenarios. In asimple confounding scenario, confounders were related to the treatment and theoutcome via parametric models. In a complex confounding scenario, the simpleconfounders were transformed to induce complicated nonlinear relationships. Inthe simple scenario, when ML algorithms were used, double-robust estimatorswere superior to single-robust estimators. In the complex scenario,single-robust estimators with ML algorithms were at least as biased asestimators using misspecified parametric models. Double-robust estimators wereless biased, but coverage was well below nominal. The use of sample splitting,inclusion of confounder interactions, reliance on a richly specified MLalgorithm, and use of doubly robust estimators was the only explored approachthat yielded negligible bias and nominal coverage. Our results suggest that MLbased singly robust methods should be avoided.

Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms