AI RESEARCH
Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects
arXiv CS.LG
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ArXi:2605.26288v1 Announce Type: cross When treatment effects are naturally expressed as ratios -- as in medicine, pricing, and marketing -- the ratio-based CATE $\tau(x) = E[Y|W=1,X=x] / E[Y|W=0,X=x]$ 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