  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.
