MATEL: Multi-Arm Treatment Effect Learner for E-commerce Packaging Optimization

Automated packaging selection at e-commerce scale assigns a pack type to every customer order, yet these decisions remain largely heuristic and cannot reason jointly about competing objectives: damage protection, material waste, labor, and transportation efficiency. We frame packaging selection as a multi-armed causal optimization problem and introduce the Multi-Arm Treatment Effect Learner (MATEL), a causal deep-learning algorithm that estimates the effect of each pack type on per-shipment damage rates from large-scale fulfillment data. MATEL advances causal effect estimation on four fronts: (1) a multi-armed causal prediction engine trained with self-calibrating loss and hybrid representation layers; (2) interchangeable state-of-the-art tabular backbones; (3) robustness to treatment sparsity, via renormalized pairwise-propensity weighting that keeps counterfactual contrasts bounded and the pairwise calibration doubly robust under overlap; and (4) transitive pairwise contrasts with bounded extrapolations for rarely-used pack types. Empirically, MATEL recovers the directional agreement of the observed pairwise treatment effect on 91% of focal pairs versus 62% for a double machine learning (DML) benchmark, while improving per-unit CATE directional agreement by $\ge 12$ pp. Its estimation keeps every per-unit effect bounded on the sparse arms where benchmarks diverge. BLP slope, GATES, and the doubly robust curve show MATEL is better calibrated for effect ranking than the benchmark. On synthetic data, MATEL achieves the lowest ATE error and best ranking against four benchmarks, and the lowest PEHE under weak and severe overlap. MATEL is deployed in production; a production-calibrated simulation estimates a $3.74$ pp network-cost improvement and a 25% reduction in corrugated-box usage, reducing packaging waste and associated carbon emissions.