Causal Responder Detection
Methodology
2024-06-26 v1 Applications
Machine Learning
Abstract
We introduce the causal responders detection (CARD), a novel method for responder analysis that identifies treated subjects who significantly respond to a treatment. Leveraging recent advances in conformal prediction, CARD employs machine learning techniques to accurately identify responders while controlling the false discovery rate in finite sample sizes. Additionally, we incorporate a propensity score adjustment to mitigate bias arising from non-random treatment allocation, enhancing the robustness of our method in observational settings. Simulation studies demonstrate that CARD effectively detects responders with high power in diverse scenarios.
Cite
@article{arxiv.2406.17571,
title = {Causal Responder Detection},
author = {Tzviel Frostig and Oshri Machluf and Amitay Kamber and Elad Berkman and Raviv Pryluk},
journal= {arXiv preprint arXiv:2406.17571},
year = {2024}
}