5 research outputs found
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Causal approaches to fairness have seen substantial recent interest, both
from the machine learning community and from wider parties interested in
ethical prediction algorithms. In no small part, this has been due to the fact
that causal models allow one to simultaneously leverage data and expert
knowledge to remove discriminatory effects from predictions. However, one of
the primary assumptions in causal modeling is that you know the causal graph.
This introduces a new opportunity for bias, caused by misspecifying the causal
model. One common way for misspecification to occur is via unmeasured
confounding: the true causal effect between variables is partially described by
unobserved quantities. In this work we design tools to assess the sensitivity
of fairness measures to this confounding for the popular class of non-linear
additive noise models (ANMs). Specifically, we give a procedure for computing
the maximum difference between two counterfactually fair predictors, where one
has become biased due to confounding. For the case of bivariate confounding our
technique can be swiftly computed via a sequence of closed-form updates. For
multivariate confounding we give an algorithm that can be efficiently solved
via automatic differentiation. We demonstrate our new sensitivity analysis
tools in real-world fairness scenarios to assess the bias arising from
confounding.Comment: published at UAI 201
Towards Sensitivity Analysis: A Workflow
Establishing causal claims is one of the primary endeavors in sociological
research. Statistical causal inference is a promising way to achieve this
through the potential outcome framework or structural causal models, which are
based on a set of identification assumptions. However, identification
assumptions are often not fully discussed in practice, which harms the validity
of causal claims. In this article, we focus on the unmeasurededness assumption
that assumes no unmeasured confounders in models, which is often violated in
practice. This article reviews a set of papers in two leading sociological
journals to check the practice of causal inference and relevant identification
assumptions, indicating the lack of discussion on sensitivity analysis methods
on unconfoundedness in practice. And then, a blueprint of how to conduct
sensitivity analysis methods on unconfoundedness is built, including six steps
of proper choices on practices of sensitivity analysis to evaluate the impacts
of unmeasured confounders