DoorDash
Causal targeting of promotions (offer personalization)
DoorDash targets its promotions with causal machine learning by estimating each customer's uplift; the personalized approach cuts cost per incremental order by nearly half at equal sales performance.
Key points
- Causal targeting of promotions: incremental effect estimated per customer, then allocation under budget.
- In-house causal framework (double machine learning, T-learner and S-learner meta-learners).
- Cost per incremental order cut by nearly half at equal lift.
- Evidence B, confirmed status, presented at the causal ML workshop at KDD 2025.
Objective
Spend the promotional budget where it actually creates additional orders, instead of subsidizing orders that would have happened without a promo.
The deployment
DoorDash uses causal machine learning to estimate the incremental effect of a promotion on each user's order probability, then chooses which promos to send under a budget constraint. The framework has two stages: first estimate the causal response per customer with methods like double machine learning and meta-learners, then optimize offer allocation. The problem it addresses is non-incremental promotions, where a discount rewards an order that would have happened anyway and burns margin. The work was presented at the causal ML workshop at KDD 2025.
Results Proof B
Technical paper from the DoorDash teams presented at the causal ML workshop at KDD 2025, with a quantified result (cost per incremental order cut by nearly two), plus a concordant official engineering post. No consolidated financial figure, hence level B.
How it works
Documented architectureThe stack in detail
- outil Framework causal in-house DoorDash Two-stage framework: per-customer uplift estimation (double machine learning, T-learner and S-learner meta-learners), presented at the causal ML workshop at KDD 2025.
- outil Optimiseur d'allocation sous contrainte Chooses which customers get which offer to maximize incremental orders within the fixed promo budget.
- infra App et CRM DoorDash (envoi des offres) Internal channels through which the targeted promotions are delivered, then the real incremental is measured to retrain the model.
How it runs, concretely
For ops teams-
1Campaign definition marketing
The marketing team sets the objective and budget of the promotional campaign.
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2Causal response estimation AI
A causal model estimates the incremental effect of the promo on each customer's order probability.
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3Allocation optimization AI
The system chooses which customers to target to maximize incremental orders under the budget constraint.
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4Send and measure data team
The targeted offers are sent, then the real incremental is measured to retrain the model.
The estimated causal uplift per customer, that is the difference in order probability with and without the promo. Without a causal measurement, correlation is mistaken for effect and the budget scatters across non-incremental orders.
How your customers perceive this type of use
Sourced studiesC'est la famille la moins acceptee : 68% des Americains jugent inacceptable un score financier personnel calcule par algorithme et 67% l'analyse video automatisee d'entretiens d'embauche (Pew Research, 2018). La demande d'explication et de recours est massive : 83% veulent savoir quelles donnees l'IA utilise et 91% veulent pouvoir corriger des donnees erronees (Consumer Reports, 2024). A l'echelle mondiale, seuls 46% se disent prets a faire confiance aux systemes d'IA et 70% jugent une regulation necessaire (KPMG / Universite de Melbourne, 2025).
Acceptance conditions
- Transparence sur les donnees utilisees : 83% des Americains la reclament (Consumer Reports 2024)
- Droit de correction des donnees erronees : 91% le demandent (Consumer Reports 2024)
- Explication de la logique de decision : 44% des consommateurs sont plus enclins a utiliser un agent IA si sa logique est clairement expliquee (Salesforce 2024)
- L'acceptabilite depend du contexte de la decision : 50% des Americains jugent equitable un score de risque criminel pour la liberation conditionnelle, contre 32% pour un score financier applique aux consommateurs (Pew Research 2018)
Red lines
- La decision opaque et sans recours sur l'emploi, le credit ou le logement : 45% tres mal a l'aise pour l'embauche, 39% pour le pret, 39% pour le logement (Consumer Reports 2024)
- Le scoring des personnes a partir de donnees comportementales : 68% le jugent inacceptable pour les offres financieres (Pew Research 2018)
Sources: Pew Research Center 2018 · Consumer Reports 2024 · KPMG / Universite de Melbourne 2025 · Salesforce 2024
How to replicate
Inference, not sourcedData prerequisites
- order history with exposure to promos
- a stable customer identifier
- results from experiments or natural variation to anchor the causal estimate
Org prerequisites
- a data science team comfortable with causal inference
- A/B testing capability
- a promo budget steerable by segment
Possible stack
- double machine learning and meta-learners for uplift
- a constrained optimizer
- a CRM sending tool
The plan, step by step
- Step 1Audit the promo history: offer exposures, orders, a stable customer identifier.Deliverable: An exposure-outcome dataset ready for modeling
- Step 2Build the uplift estimation (double machine learning or meta-learners) on a past promo.Deliverable: An uplift model validated offline (uplift by decile, Qini curves)
- Step 3Add budget-constrained allocation optimization and define the test campaign.Deliverable: A campaign plan: an uplift-targeted arm against a uniform arm, fixed budget
- Step 4Run the controlled test, uplift-based targeting against a uniform send.Deliverable: A read on cost per incremental order for both arms
- Step 5Industrialize: retraining per campaign and systematic incremental measurement.Deliverable: A reusable causal promo pipeline for every campaign
First step: Estimate the causal uplift of an existing promo on a segment, then compare uplift-based targeting to a uniform send.
The same pattern, proven in other industries
American Express
real-time fraud detection with deep learning
Lemonade
AI-driven underwriting and claims management
Mastercard
real-time transaction risk scoring
Sources
- S1 Causal Machine Learning for Promotions: Industry Evidence and Applications (KDD 2025 Workshop) Primary archive pending
- S2 Smarter promotions with causal machine learning Interested party archive pending
An error, newer info, a source?
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