Double machine learning to estimate the effects of multiple treatments and their interactions

Thomas M. Menino Convention & Exhibition Center 

Causal inference literature has extensively focused on binary treatments, with increasing attention to multi-valued treatments. However, methods for multiple simultaneously assigned treatments are still understudied. This paper introduces two settings: (1) estimating the effects of multiple concurrent treatments of different types (binary, categorical, and continuous) and the effects of treatment interactions, and (2) estimating the average treatment effect across categories of multi-valued regimens. To obtain robust estimates for both settings, we propose a class of methods based on the double machine learning framework. We use machine learning to flexibly model confounding relationships, which can introduce bias in estimating treatment effects due to regularization bias and overfitting. Our methods overcome such bias through Neyman orthogonality and cross-fitting, and are thus well-suited for complex settings with multiple treatments or regimens. We apply the methods to study the effect of three treatments on HIV-related disease. To our knowledge, this work is the first to apply machine learning for robust estimation of interaction effects in the presence of multiple treatments.

Causal inference

Machine learning

Multiple treatments

Observational data

Semiparametric model 

Biometrics Section