Orthogonal machine learning for conditional odds and risk ratios
Conditional effects are commonly used measures for understanding how treatment effects vary across different groups, and are often used to target treatments/interventions to groups who benefit most. In this work we review existing methods and propose novel ones, focusing on the odds ratio (OR) and the risk ratio (RR). While estimation of the conditional average treatment effect (ATE) has been widely studied, estimators for the OR and RR lag behind, and cutting edge estimators such as those based on doubly robust transformations or orthogonal risk functions have not been generalized to these parameters. We propose such a generalization here, focusing on the DR-learner and the R-learner. We...
| Authors | Jiacheng Ge, Iván Díaz |
|---|---|
| Primary category | stat.ML |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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