Causal Responder Detection

Tzviel Frostig; Oshri Machluf; Amitay Kamber; Elad Berkman; Raviv Pryluk

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arXiv · 2406.17571

Causal Responder Detection

Tzviel Frostig·Oshri Machluf·Amitay Kamber·Elad Berkman·Raviv Pryluk

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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.

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Tzviel Frostig, Oshri Machluf, Amitay Kamber, Elad Berkman, Raviv Pryluk. 2024-06-25. Causal Responder Detection. https://arxiv.org/abs/2406.17571

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