  How do we learn from biased data? Historical datasets often reflect
historical prejudices; sensitive or protected attributes may affect the
observed treatments and outcomes. Classification algorithms tasked with
predicting outcomes accurately from these datasets tend to replicate these
biases. We advocate a causal modeling approach to learning from biased data,
exploring the relationship between fair classification and intervention. We
propose a causal model in which the sensitive attribute confounds both the
treatment and the outcome. Building on prior work in deep learning and
generative modeling, we describe how to learn the parameters of this causal
model from observational data alone, even in the presence of unobserved
confounders. We show experimentally that fairness-aware causal modeling
provides better estimates of the causal effects between the sensitive
attribute, the treatment, and the outcome. We further present evidence that
estimating these causal effects can help learn policies that are both more
accurate and fair, when presented with a historically biased dataset.
