Quantifying causality in data science with quasi-experiments

Estimating causality from observational data is essential in many data science questions but can be a challenging task. Here we review approaches to causality that are popular in econometrics and that exploit (quasi) random variation in existing data, called quasi-experiments, and show how they can be combined with machine learning to answer causal questions within typical data science settings. We also highlight how data scientists can help advance these methods to bring causal estimation to high-dimensional data from medicine, industry and society. While estimating causality from observational data is challenging, quasi-experiments provide causal inference methods with plausible assumptions that can be practical to a range of real-world problems.