You have logged, observational data from an always-on promotion and want to target users to maximize incremental revenue. Propose a causal ML approach to estimate heterogeneous treatment effects (HTE) and derive a targeting policy. Specify: (a) choose among T-/S-/X-/DR-learners and justify with bias–variance and ignorability considerations; (b) model choices and cross-fitting to reduce overfitting and nuisance bias; (c) handling positivity/overlap violations and extreme propensities; (d) evaluation metrics (policy value estimation, uplift/Qini, PEHE proxy) and how to implement off-policy evaluation with IPW and doubly robust estimators; (e) fairness constraints (e.g., demographic parity of treatment) and their impact on policy; (f) how you would A/B test the learned policy safely before full deployment.

This question evaluates a candidate's competency in causal machine learning, heterogeneous treatment effect estimation, off-policy evaluation, and fairness-aware policy design using observational data.

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Estimate heterogeneous treatment effects with causal ML

Context

You are given large-scale, logged observational data from an always-on promotion. Each record contains features X (user/context), a binary treatment A (promotion shown or not), and an outcome Y (e.g., revenue in the next 7 days). Treatment assignment was not randomized and may depend on X.

Goal: Learn heterogeneous treatment effects (HTE) to target users and maximize incremental revenue with a safe, fair, and evaluable deployment plan.

Assumptions (state and justify any deviations):

Binary treatment A ∈ {0,1}. Outcome Y is measured over a fixed horizon after exposure and is not affected by future actions (no carryover beyond window). No interference across users. Unconfoundedness given observed X is assumed plausible.
If promotion has a known cost c per exposure, the policy should maximize net incremental revenue (uplift minus cost).

Task

Propose a causal ML approach that addresses the following:

(a) Choose among T-/S-/X-/DR-learners for HTE, and justify your choice using bias–variance and ignorability considerations.

(b) Specify modeling choices for nuisance functions and HTE, and how you will use cross-fitting to reduce overfitting and nuisance bias.

(d) Define evaluation metrics (policy value estimation, uplift/Qini, PEHE proxy) and explain how to implement off-policy evaluation with IPW and doubly robust estimators.

(e) Propose fairness constraints (e.g., demographic parity of treatment) and explain how they affect the learned policy.

(f) Outline how you would A/B test the learned policy safely before full deployment.

Context

Goal: Learn heterogeneous treatment effects (HTE) to target users and maximize incremental revenue with a safe, fair, and evaluable deployment plan.

Assumptions (state and justify any deviations):

Binary treatment A ∈ {0,1}. Outcome Y is measured over a fixed horizon after exposure and is not affected by future actions (no carryover beyond window). No interference across users. Unconfoundedness given observed X is assumed plausible.

If promotion has a known cost c per exposure, the policy should maximize net incremental revenue (uplift minus cost).

Task

Propose a causal ML approach that addresses the following:

(a) Choose among T-/S-/X-/DR-learners for HTE, and justify your choice using bias–variance and ignorability considerations.

(b) Specify modeling choices for nuisance functions and HTE, and how you will use cross-fitting to reduce overfitting and nuisance bias.

(d) Define evaluation metrics (policy value estimation, uplift/Qini, PEHE proxy) and explain how to implement off-policy evaluation with IPW and doubly robust estimators.

(e) Propose fairness constraints (e.g., demographic parity of treatment) and explain how they affect the learned policy.

(f) Outline how you would A/B test the learned policy safely before full deployment.

Estimate heterogeneous treatment effects with causal ML

Quick Overview

Context

Task

Solution

Submit Your Answer to Earn 20XP

Estimate heterogeneous treatment effects with causal ML

Quick Overview

Context

Task

Solution

Submit Your Answer to Earn 20XP