<ul><li>This work discusses the impact of missing outcome data on the estimation of treatment effects.</li><li>The authors propose two de-biased machine learning estimators for the conditional average treatment effect (CATE).</li><li>The mDR-learner and mEP-learner integrate inverse probability of censoring weights to address under-representation.</li><li>The performance of these estimators is illustrated through simulated data settings and compared to existing CATE estimators.</li></ul>

Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data

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