  Background. Starting from the 1960s, practitioners and researchers have
looked for ways to empirically investigate new technologies such as inspecting
the effectiveness of new methods, tools, or practices. With this purpose, the
empirical software engineering domain started to identify different empirical
methods, borrowing them from various domains such as medicine, biology, and
psychology. Nowadays, a variety of empirical methods are commonly applied in
software engineering, ranging from controlled and quasi-controlled experiments
to case studies, from systematic literature reviews to the newly introduced
multivocal literature reviews. However, to date, the only available method for
proving any cause-effect relationship are controlled experiments. Objectives.
The goal of the thesis is introducing new methodologies for studying causality
in empirical software engineering. Methods. Other fields use observational
studies for proving causality. They allow observing the effect of a risk factor
and testing this without trying to change who is or is not exposed to it. As an
example, with an observational study it is possible to observe the effect of
pollution on the growth of a forest or the effect of different factors on
development productivity without the need of waiting years for the forest to
grow or exposing developers to a specific treatment. Conclusion. In this
thesis, we aim at defining a methodology for applying observational studies in
empirical software engineering, providing guidelines on how to conduct such
studies, how to analyze the data, and how to report the studies themselves.
