arXiv:2605.26288v1 Announce Type: cross Abstract: 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 introduce the Q-Learner, which decomposes $\tau(x)$ into a product of two odds ratios, reducing ratio-CATE estimation for binary outcomes to two propensity classification tasks. We further derive doubly r
The paper addresses a current limitation in causal inference, specifically for ratio-based treatment effects, indicating an ongoing push for more robust and accurate AI/ML methodologies.
This development allows for more precise and reliable measurement of treatment effects in fields where outcomes are naturally expressed as ratios, such as medicine and marketing, improving decision-making accuracy.
Existing methodologies for ratio-based treatment effect estimation, often relying on parametric assumptions or lacking robustness guarantees, will be augmented or replaced by more accurate and provably robust methods like the Q-Learner.
- · AI/ML researchers
- · Healthcare providers
- · Marketing analytics firms
- · Economic modelers
- · Less robust causal inference models
- · Organizations relying solely on generic regression for ratio effects
Improved accuracy in quantifying treatment effects in fields like medicine and commerce.
More effective and personalized interventions or strategies by correctly identifying the impact of treatments.
Enhanced trust and broader adoption of AI-driven causal inference techniques across sensitive industries.
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Read at arXiv cs.LG