logistics_ops · workflow
DoorDash supercharges delivery logistics through causal ML and joint optimization of supply and assignment
DoorDash's supply and assignment systems operated independently, causing inefficiencies: static supply targets led the system to falsely conclude there were no more improvements once targets were hit, and peak pay causal effect estimates were inherently biased because they were not derived through a causal ML framework.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Demand and supply forecasting
Regular ML models forecast demand and baseline supply hours for all geo and time units over a few days.
Tools used
LightGBMR-learner
Outcome
Multiple A/B tests validated that the two-stage joint optimization system significantly improves delivery quality and reduces total cost.
What failed first
Fitting a standard ML model on observational data produced biased causal estimates because peak pay correlates with undersupply conditions, and the double ML approach was also sub-optimal due to unmet assumptions about confounding variables.
Results
Cost replacedreduces total cost
Grounding & classification
Source type: technical build writeup
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forecastingpredictive analyticsfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedecommercelogisticscost reductioncycle time reductiontechnical build writeuplogistics opssupply chain