The science of cause and effect is extremely sophisticated and extremely hard to scale. Using a controlled experiment, scientists get rich insights by analyzing global effects, effects in different segments, and trends in effects over time. They use propensity scores to project external validity. To support the analysis of relative effects, scientists derive challenging ratio distributions. While the analytical capabilities in experimentation are advancing, we require new innovation within engineering and computational causal inference to enable an experimentation platform to make analyses performant and scalable. Of significant importance: we must unify the computing strategy for these models so that they can be consistently applied across experiments. In doing so, the industry can make significant progress towards developing a flywheel that unifies and accelerates the evaluation and roll out of experiments. In order to support unified computation, this paper introduces baseline vectors and delta vectors as common structure for estimating treatment effects. This common structure allows many statistics to be subsumed into a single API. The nature of its algebraic formulation allows linear algebra libraries to vectorize and optimize its performance, creating a single and efficient tool to support the many innovations in experimentation.

翻译：因果科学极其精密且难以扩展。通过控制实验，科学家通过分析全局效应、不同区段内的效应以及效应随时间的变化趋势获得丰富洞见。他们使用倾向性评分来评估外部效度。为支持相对效应分析，科学家需推导复杂的比率分布。尽管实验分析能力不断进步，我们仍需在工程与计算因果推断领域进行创新，以使实验平台的分析具备高性能与可扩展性。至关重要的是：我们必须统一这些模型的计算策略，使其能够跨实验一致应用。通过此举，行业可朝着构建统一并加速实验评估与部署的飞轮机制取得重大进展。为支持统一计算，本文引入基线向量与delta vectors作为估计处理效应的通用结构。该通用结构使得众多统计量可整合至单一API中。其代数表达形式允许线性代数库进行向量化与性能优化，从而创建支持实验领域诸多创新成果的单一高效工具。