# Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash

arXiv: https://arxiv.org/abs/2311.14698 · HTML: https://mlsanity.com/paper/2311.14698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Targeting for long-term outcomes](https://mlsanity.com/paper/2010.15835/) (2020)
- [Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis](https://mlsanity.com/paper/2201.01357/) (2022)
- [Test & Roll: Profit-Maximizing A/B Tests](https://mlsanity.com/paper/1811.00457/) (2018)

## Abstract (arXiv)

This paper investigates an approach to both speed up business decision-making and lower the cost of learning through experimentation by factorizing business policies and employing fractional factorial experimental designs for their evaluation. We illustrate how this method integrates with advances in the estimation of heterogeneous treatment effects, elaborating on its advantages and foundational assumptions. We empirically demonstrate the implementation and benefits of our approach and assess its validity in evaluating consumer promotion policies at DoorDash, which is one of the largest delivery platforms in the US. Our approach discovers a policy with 5% incremental profit at 67% lower implementation cost.