A comparison of causal inference methods for evaluating multiple treatment groups

Causal inference is formulated using the counterfactual framework, enabling direct investigation of causal questions. Causal inference methods can incorporate machine learning techniques into the estimation process, allowing for more flexible models. However, the integration of machine learning methods adds complexity to statistical inference. In this paper, we systematically assess several methods for making causal inference with multiple treatment groups, including the outcome regression, inverse propensity score weighting, double-robust estimators, and their counterparts when employing a super learner in the estimation process, as well as the targeted maximum likelihood estimator (TMLE). We conduct numerical studies with complex data-generating models to evaluate these different estimators. Our results suggest that the double-robust estimator, when combined with machine learning, is the most favourable approach, demonstrating lower biases, a valid variance estimator, and improved coverage probabilities for the 95% confidence interval.