Fast Fair Regression via Efficient Approximations of Mutual Information

1 code implementation • 14 Feb 2020 • Daniel Steinberg, Alistair Reid, Simon O'Callaghan, Finnian Lattimore, Lachlan McCalman, Tiberio Caetano

Most work in algorithmic fairness to date has focused on discrete outcomes, such as deciding whether to grant someone a loan or not.

Computational Efficiency Fairness +1

Causal inference with Bayes rule

no code implementations • 2 Oct 2019 • Finnian Lattimore, David Rohde

The concept of causality has a controversial history.

Causal Inference

Replacing the do-calculus with Bayes rule

no code implementations • 17 Jun 2019 • Finnian Lattimore, David Rohde

The concept of causality has a controversial history.

A Primer on Causal Analysis

no code implementations • 5 Jun 2018 • Finnian Lattimore, Cheng Soon Ong

We provide a conceptual map to navigate causal analysis problems.

Navigate

The Crossover Process: Learnability and Data Protection from Inference Attacks

no code implementations • 13 Jun 2016 • Richard Nock, Giorgio Patrini, Finnian Lattimore, Tiberio Caetano

It is usual to consider data protection and learnability as conflicting objectives.

Causal Inference Fairness

Causal Bandits: Learning Good Interventions via Causal Inference

no code implementations • NeurIPS 2016 • Finnian Lattimore, Tor Lattimore, Mark D. Reid

We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment.

Causal Inference