  Causal inference methods are widely applied in the fields of medicine,
policy, and economics. Central to these applications is the estimation of
treatment effects to make decisions. Current methods make binary yes-or-no
decisions based on the treatment effect of a single outcome dimension. These
methods are unable to capture continuous space treatment policies with a
measure of intensity. They also lack the capacity to consider the complexity of
treatment such as matching candidate treatments with the subject. We propose to
formulate the effectiveness of treatment as a parametrizable model, expanding
to a multitude of treatment intensities and complexities through the continuous
policy treatment function, and the likelihood of matching. Our proposal to
decompose treatment effect functions into effectiveness factors presents a
framework to model a rich space of actions using causal inference. We utilize
deep learning to optimize the desired holistic metric space instead of
predicting single-dimensional treatment counterfactual. This approach employs a
population-wide effectiveness measure and significantly improves the overall
effectiveness of the model. The performance of our algorithms is. demonstrated
with experiments. When using generic continuous space treatments and matching
architecture, we observe a 41% improvement upon prior art with
cost-effectiveness and 68% improvement upon a similar method in the average
treatment effect. The algorithms capture subtle variations in treatment space,
structures the efficient optimizations techniques, and opens up the arena for
many applications.

Machine Learning;Artificial Intelligence;Machine Learning