Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects
When treatment effects are naturally expressed as ratios -- as in medicine, pricing, and marketing -- the ratio-based CATE $τ(x) = E[Y|W=1,X=x] / E[Y|W=0,X=x]$ is the appropriate estimand. Yet existing estimators either impose a log-linear parametric structure or apply generic regression without robustness guarantees for this functional. We introduce the Q-Learner, which decomposes $τ(x)$ into a product of two odds ratios, reducing ratio-CATE estimation for binary outcomes to two propensity classification tasks. We further derive doubly robust augmentations for both S/T- and Q-style ratio learners and characterize their distinct robustness properties. In benchmarks on seven RCT datasets, ...
| Authors | Michael Fuchs, Dominik Kreiss |
|---|---|
| Primary category | stat.ML |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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