5 papers
Preference-based Conditional Treatment Effects and Policy Learning
Dovid Parnas, Mathieu Even, Julie Josse +1
We introduce a new preference-based framework for conditional treatment effect estimation and policy learning, built on the Conditional Preference-based Treatment Effect (CPTE). CP…
From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
Rom Gutman, Shimon Sheiba, Omer Noy Klein +5
We propose a framework for building patient-specific treatment recommendation models, building on the large recent literature on learning patient-level causal models and inspired b…
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova +2
Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which…
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions
Omer Noy Klein, Alihan Hüyük, Ron Shamir +2
Randomized Controlled Trials (RCTs) are the gold standard for evaluating the effect of new medical treatments. Treatments must pass stringent regulatory conditions in order to be a…
On the ERM Principle in Meta-Learning
Yannay Alon, Steve Hanneke, Shay Moran +1
Classic supervised learning involves algorithms trained on labeled examples to produce a hypothesis aimed at performing well on unseen examples. Meta-learni…