Design Experiments for Causal Inference in Marketing Analytics

Q: Design Experiments for Causal Inference in Marketing Analytics

This question evaluates expertise in experimental design and causal inference within marketing analytics, including practical tooling, real-world project application, Difference-in-Differences setup and interpretation, and end-to-end experiment analysis.

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Question

Technical Phone Screen: Marketing Experiments and Causal Inference

Prompt

You are interviewing for a data-science role focusing on marketing experiment design and causal inference.

Answer the following:

Tooling

Which Python or R packages do you use for causal inference and experiment analysis, and why?

Project Example

Describe a project where you applied causal-inference methods.
- What was the business problem?
- Which approach did you choose and why?
- What was the impact?

Difference-in-Differences (DiD)

Explain the DiD technique: setup, estimator, and interpretation.
What key assumptions does it rely on?
When would you prefer DiD over other causal methods?

Email Campaign for the 1point3acres Community

How would you: a) Select target users? b) Define success metrics (primary/secondary)? c) Design a screening test and a hold-out experiment? d) Analyze the results (power, lift, significance), including guardrails and diagnostics?

Hints: Mention packages like statsmodels, EconML; cover parallel trends, treatment vs. control, randomization, power, lift, and significance.

Design Experiments for Causal Inference in Marketing Analytics

Technical Phone Screen: Marketing Experiments and Causal Inference

Prompt

Solution

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