TaQFormer: A Treatment-as-Query Transformer with Data-Fusion for Multidimensional Treatment Modeling in Coupon Allocation

Key points

• We study coupon allocation on an online marketplace offering multiple service categories.1
• Issuing coupons requires choosing a vector of category-specific discounts, accounting for both their direct effects and cross-category demand shifts.1
• To combine large but policy-biased observational logs with a smaller stream of randomized controlled trial data, we use a two-stage pipeline: observational pretraining learns general outcome patterns, followed by LoRA fine-tuning on RCT data with a request-specific fusion gate.1
• The deployment illustrates how multidimensional treatment modeling and observational pretraining, combined with adaptation using RCT data, can support effective joint discount decisions in production.1

Sources (1)

• [1]TaQFormer: A Treatment-as-Query Transformer with Data-Fusion for Multidimensional Treatment Modeling in Coupon Allocation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 11:27 AM

  We present TaQFormer, a treatment-as-query Transformer for this multidimensional treatment setting.

  We study coupon allocation on an online marketplace offering multiple service categories.

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