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What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Onsite rounds at Uber.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Uber during technical interviews.

Build and deploy an uplift targeting model

Quick Overview

This question evaluates a candidate's ability to design and deploy uplift/causal targeting models, covering causal inference, uplift estimation, pre-treatment feature engineering and leakage prevention, policy optimization under budget and capacity constraints, offline and online evaluation, fairness considerations, and fraud mitigation.

Uplift Modeling and Policy Design for Free Trial/Bonus Targeting

You ran a past randomized test that offered some users a free trial/bonus (treatment) while others received no offer (control). You want to learn who should receive the offer going forward to maximize incremental business value under budget and capacity constraints.

Design an end-to-end solution that:

1) Problem setup and labels

Define the objective, treatment, outcome window, and the incremental metric to optimize (e.g., net profit).
Construct training labels using only the past randomized test, clarifying intent-to-treat vs. treatment-on-the-treated.

2) Features (pre-treatment only)

Specify pre-treatment feature groups and concrete examples. Call out any that risk leakage and how you will exclude them.

3) Modeling approach and regularization

Choose and justify one uplift/causal modeling strategy (e.g., two-model T-learner, S-learner, X-/DR-learner, causal forests). Include base learners, cross-fitting, and regularization choices.
Describe calibration and how you will handle class imbalance and outcome scaling (binary vs. continuous profit).

4) Offline evaluation

Show how you would compute and interpret Qini curves/uplift AUC.
Describe doubly-robust policy value estimation for a targeting policy.
Explain how to incorporate treatment-capacity (top-K) and budget constraints (variable per-user costs).

5) Fairness and fraud guardrails

Define fairness slices, metrics, and any constraints/threshold adjustments.
Identify fraud/abuse risks (e.g., multi-accounting) and detection/mitigation tactics.

6) Online A/B policy test and safe ramp-up

Propose an experiment design to compare the learned policy to a control/heuristic, including holdouts, interference considerations, ramp plan, and stop-loss rules.

7) Preventing leakage and handling cold-start

List concrete steps to prevent leakage of post-treatment information in training and scoring.
Propose approaches for cold-start users with little or no history.

Quick Overview

Uplift Modeling and Policy Design for Free Trial/Bonus Targeting

Design an end-to-end solution that:

3) Modeling approach and regularization

Choose and justify one uplift/causal modeling strategy (e.g., two-model T-learner, S-learner, X-/DR-learner, causal forests). Include base learners, cross-fitting, and regularization choices.

Describe calibration and how you will handle class imbalance and outcome scaling (binary vs. continuous profit).

Build and deploy an uplift targeting model

Quick Overview

Uplift Modeling and Policy Design for Free Trial/Bonus Targeting

1) Problem setup and labels

2) Features (pre-treatment only)

3) Modeling approach and regularization

4) Offline evaluation

5) Fairness and fraud guardrails

6) Online A/B policy test and safe ramp-up

7) Preventing leakage and handling cold-start

Solution

Submit Your Answer

Build and deploy an uplift targeting model

Quick Overview

Uplift Modeling and Policy Design for Free Trial/Bonus Targeting

1) Problem setup and labels

2) Features (pre-treatment only)

3) Modeling approach and regularization

4) Offline evaluation

5) Fairness and fraud guardrails

6) Online A/B policy test and safe ramp-up

7) Preventing leakage and handling cold-start

Solution

Submit Your Answer