Abstract

Many questions in data science are fundamentally causal questions, such as the impact of a marketing campaign or a new product feature, the reasons for customer churn, which drug may work best for which patient, and so on. As the field of data science has grown, many practitioners are realizing the value of causal inference in providing insights from data. However, unlike the streamlined experience for supervised machine learning with libraries like Tensorflow (Abadi et al. 2016) and PyTorch (Paszke et al. 2019), it is non-trivial to build a causal inference analysis. Software libraries that implement state-of-the art causal inference methods can accelerate the adoption of causal inference among data analysts in both industry and academia.