An empirical study of the effects of unconfoundedness on the performance of Propensity Score Matching

The purpose of this research is to analyze the performance of Propensity Score Matching, a causal inference method for causal effect estimation. More specifically, investigate how Propensity Score Matching reacts to breaking the unconfoundedness assumption, one of its core conceptual pillars. This has been achieved by running PSM on synthetic...

Causal Fairness of Machine Learning: Bridging Individual- and Population-Level Fairness Methods

As Machine Learning models are being applied to a wide range of fields, the potential impact that these algorithms can have on people's lives is increasing. In a growing number of applications, such as criminal justice, financial assessments, job and college applications, the data points are indeed people's profiles. Therefore, in the presence...