Directed Acyclic Graphs and Bayesian Networks for Causal Identification and Estimation
The objective of this paper is to provide you with a practical framework for causal effect estimation in the context of policy assessment and impact analysis, and in the absence of experimental data. We will present a range of methods, along with their limitations, including Directed Acyclic Graphs and Bayesian networks. These techniques are intended to help you distinguish causation from association when working with data from observational studies.
This paper is structured as a tutorial that revolves around a single, seemingly simple example. On the basis of this example, we will illustrate numerous techniques for causal identification and estimation.
Major government or business initiatives generally involve extensive studies to anticipate consequences of actions not yet taken. Such studies are often referred to as “policy analysis” or “impact assessment.”
Rubin (1974) and Holland (1986), who introduced the counterfactual (potential outcomes) approach to causal inference to statistics, can be credited with overcoming statisticians’ traditional reluctance to engage causality. However, it will take many years for this fairly recent academic consensus to fully reach the world of practitioners, which is the motivation for this paper. We wish to make the important advances in causality accessible to analysts, whose work ultimately drives the policies that shape our world.