  Much of our experiments are designed to uncover the cause(s) and effect(s)
behind a data generating mechanism (i.e., phenomenon) we happen to be
interested in. Uncovering such relationships allows us to identify the true
working of a phenomenon and, most importantly, articulate a model that may
enable us to further explore the phenomenon on hand and/or allow us to predict
it accurately. Fundamentally, such models are likely to be derived via a causal
approach (as opposed to an observational or empirical mean). In this approach,
causal discovery is required to create a causal model, which can then be
applied to infer the influence of interventions, and answer any hypothetical
questions (i.e., in the form of What ifs? Etc.) that we might have. This paper
builds a case for causal discovery and causal inference and contrasts that
against traditional machine learning approaches; all from a civil and
structural engineering perspective. More specifically, this paper outlines the
key principles of causality and the most commonly used algorithms and packages
for causal discovery and causal inference. Finally, this paper also presents a
series of examples and case studies of how causal concepts can be adopted for
our domain.
