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We have presented CIL method to learn the optimal dynamic treatment regime by exploiting information from both trajectories (positive and negative).
This repository contains code to estimate sample size needed to compare dynamic treatment regimens using longitudinal count outcomes from a Sequential Multiple Assignment Randomized Trial (SMART).
Learning Dynamic Treatment Regime (DTR) via meta-learners
Companion GitHub repo for the following paper: https://arxiv.org/abs/2211.16393