tlverse/causalglm: Interpretable and robust causal inference for heterogeneous treatment effects using generalized linear models with targeted machine-learning.

Utilizing the framework of Targeted Maximum-Likelihood estimation (TMLE) and machine-learning, robust and efficient estimates and inference can be obtained for user-specified semiparametric and nonparametric generalized linear models including: Conditional odds ratios between a binary outcome and binary treatment variables (causal semiparametric logisic regression) Conditional additive treatment effects for a continuous outcome (causal semiparametric linear regression with general link functions) Conditional relative risk/treatment-effects for a nonnegative outcome (e.g. binary or count) (causal semiparametric relative risk regression with general link functions)

install.packages("remotes")
remotes::install_github("tlverse/causalglm")