Computational Causal Inference
It addresses the need for scalable and robust causal inference tools for researchers and engineers, though it is incremental in proposing a new field rather than a specific method.
The paper introduces computational causal inference as an interdisciplinary field focused on developing software to analyze massive datasets for causal effects, aiming to improve research agility and integration into engineering systems.
We introduce computational causal inference as an interdisciplinary field across causal inference, algorithms design and numerical computing. The field aims to develop software specializing in causal inference that can analyze massive datasets with a variety of causal effects, in a performant, general, and robust way. The focus on software improves research agility, and enables causal inference to be easily integrated into large engineering systems. In particular, we use computational causal inference to deepen the relationship between causal inference, online experimentation, and algorithmic decision making. This paper describes the new field, the demand, opportunities for scalability, open challenges, and begins the discussion for how the community can unite to solve challenges for scaling causal inference and decision making.