Minimax gradient boosting in causal inference

Estimating causal effects is a critical task in numerous scientific and practical fields, as it allows for a deeper understanding of the impact of interventions or treatments. While a variety of machine learning techniques have been employed to determine causal effects, there is a pressing need for more versatile and dependable methodologies. In this study, we propose an innovative, generalized gradient boosting framework specifically designed for tackling minimax modelling in causal inference.