# Improving Fair Predictions Using Variational Inference In Causal Models

arXiv: https://arxiv.org/abs/2008.10880 · HTML: https://mlsanity.com/paper/2008.10880/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Causal Deep Learning](https://mlsanity.com/paper/2303.02186/) (2023)
- [Training a Resilient Q-Network against Observational Interference](https://mlsanity.com/paper/2102.09677/) (2021)
- [An Interpretable Graph-based Mapping of Trustworthy Machine Learning Research](https://mlsanity.com/paper/2105.06591/) (2021)

## Abstract (arXiv)

The importance of algorithmic fairness grows with the increasing impact machine learning has on people's lives. Recent work on fairness metrics shows the need for causal reasoning in fairness constraints. In this work, a practical method named FairTrade is proposed for creating flexible prediction models which integrate fairness constraints on sensitive causal paths. The method uses recent advances in variational inference in order to account for unobserved confounders. Further, a method outline is proposed which uses the causal mechanism estimates to audit black box models. Experiments are conducted on simulated data and on a real dataset in the context of detecting unlawful social welfare. This research aims to contribute to machine learning techniques which honour our ethical and legal boundaries.