31 citations · 63 across the 7 of their papers we have counts for
7 papers
Fairness under uncertainty in sequential decisions
Michelle Seng Ah Lee, Kirtan Padh, David Watson +2
Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches alone cannot resolve structural…
Cluster-Dags as Powerful Background Knowledge For Causal Discovery
Jan Marco Ruiz de Vargas, Kirtan Padh, Niki Kilbertus
Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. H…
Multi-disciplinary fairness considerations in machine learning for clinical trials
Isabel Chien, Nina Deliu, Richard E. Turner +3
While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A n…
Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh, Jakob Zeitler, David Watson +3
Causal effect estimation is important for many tasks in the natural and social sciences. We design algorithms for the continuous partial identification problem: bounding the effect…
A Class of Algorithms for General Instrumental Variable Models
Niki Kilbertus, Matt J. Kusner, Ricardo Silva
Causal treatment effect estimation is a key problem that arises in a variety of real-world settings, from personalized medicine to governmental policy making. There has been a flur…
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Niki Kilbertus, Philip J. Ball, Matt J. Kusner +2
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…
Blind Justice: Fairness with Encrypted Sensitive Attributes
Niki Kilbertus, Adrià Gascón, Matt J. Kusner +3
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender…