Applications of statistical causal inference in software engineering

This paper focuses on the application of one type of empirical methods, namely statistical causal inference (SCI, see section 2). Such methods have their roots in a number of applied fields (from AI to econometrics) and aim to provide a framework for making valid inferences about causal effects based on interventional or observational data. More specifically, we focus on SCI methods that use graphical models as developed by Pearl and colleagues [1, 2]. This framework has been shown to be equivalent of the potential-outcomes framework (also called the Neyman-Rubin Causal Model [3]) but enriches it by making use of an explicit causal structure called a graphical causal model. Making assumptions about causal effects explicit through a graphical structure has several advantages. First, it helps determine whether causal effects can be estimated and how they might be estimated (see section 2).