  Machine learning algorithms are useful for various predictions tasks, but
they can also learn how to discriminate, based on gender, race or other
sensitive attributes. This realization gave rise to the field of fair machine
learning, which aims to measure and mitigate such algorithmic bias. This
manuscript describes the R-package fairadapt, which implements a causal
inference pre-processing method. By making use of a causal graphical model and
the observed data, the method can be used to address hypothetical questions of
the form "What would my salary have been, had I been of a different
gender/race?". Such individual level counterfactual reasoning can help
eliminate discrimination and help justify fair decisions. We also discuss
appropriate relaxations which assume certain causal pathways from the sensitive
attribute to the outcome are not discriminatory.
