5 papers
Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models
Jessica Lally, Milad Kazemi, Nicola Paoletti +2
Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from latent variables. However, Markov D…
On the QUEST for Uncertainty Quantification via Highest Density Regions
Sam Goring, Tom Kuipers, Nicola Paoletti +1
Uncertainty quantification (UQ) is essential for reliable decision-making in safety-critical applications in probabilistic machine learning. For regression problems, dominant scala…
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…
BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments
Jordan Penn, Lee M. Gunderson, Gecia Bravo-Hermsdorff +2
Instrumental variables (IVs) are widely used to estimate causal effects in the presence of unobserved confounding between exposure and outcome. An IV must affect the outcome exclus…
Bounding Causal Effects with Leaky Instruments
David S. Watson, Jordan Penn, Lee M. Gunderson +3
Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical approaches rely on strong a…