Excited to share our new paper! Causal-ML can potentially dramatically improve the detection of responders in clinical data, but maintaining statistical guarantees is hard! Read our paper to see our approach to overcome this... Great work PhaseV Elad Berkman Tzviel Frostig Oshri Machluf Amitay Kamber
We're pleased to introduce our novel Causal Responders Detection (CARD) method, detailed in our latest publication. CARD applies machine learning and causal tree techniques to identify individuals who benefit significantly from treatments, ensuring false discovery control in both RCTs and observational studies. This method refines our understanding of individual treatment responses, providing crucial insights for precision medicine development. Our simulations confirm CARD's effectiveness across various settings, enabling to apply this method across therapeutic areas and clinical phases. #PersonalizedMedicine #MachineLearning #ClinicalTrials #HealthcareInnovation Tzviel Frostig Oshri Machluf Elad Berkman Raviv Pryluk Amitay Kamber
Inspiring!
Congratulations on the publication! This is a significant contribution to the field. The challenge of balancing causal inference with statistical rigor is no small feat—looking forward to diving into your innovative approach!
Amazing work on CARD!
CEO & Co-founder at Protai
2moNice work! 😀 Raviv Pryluk Amitay Kamber Elad Berkman