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Causal inference methods are widely applied in the fields of medicine,policy, and economics. Central to these applications is the estimation oftreatment effects to make decisions. Current methods make binary yes-or-nodecisions based on the treatment effect of a single outcome dimension. Thesemethods are unable to capture continuous space treatment policies with ameasure of intensity. They also lack the capacity to consider the complexity oftreatment such as matching candidate treatments with the subject. We propose toformulate the effectiveness of treatment as a parametrizable model, expandingto a multitude of treatment intensities and complexities through the continuouspolicy treatment function, and the likelihood of matching. Our proposal todecompose treatment effect functions into effectiveness factors presents aframework to model a rich space of actions using causal inference. We utilizedeep learning to optimize the desired holistic metric space instead ofpredicting single-dimensional treatment counterfactual. This approach employs apopulation-wide effectiveness measure and significantly improves the overalleffectiveness of the model. The performance of our algorithms is. demonstratedwith experiments. When using generic continuous space treatments and matchingarchitecture, we observe a 41% improvement upon prior art withcost-effectiveness and 68% improvement upon a similar method in the averagetreatment effect. The algorithms capture subtle variations in treatment space,structures the efficient optimizations techniques, and opens up the arena formany applications.

Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects