Explain Causal-Inference Techniques in Your Machine Learning Project

Q: Explain Causal-Inference Techniques in Your Machine Learning Project

This question evaluates a data scientist's competency in causal inference within machine learning projects, assessing understanding of treatment definition, identification strategies (e.g.

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Question

Technical Deep-Dive: ML Project With Causal Inference

Prompt

Walk me through one machine-learning project you led and explain any causal-inference techniques you applied.

What to cover (3–5 minutes, then be ready to dive deeper)

Problem and business metric.
Data and “treatment” definition; key features and outcome.
Model selection and why (baseline vs advanced, offline metrics).
Causal method and identification (e.g., propensity scores, DiD, AIPW, IV); assumptions.
Results and validation; diagnostics and sensitivity checks.
Lessons learned and what you’d do next.

Explain Causal-Inference Techniques in Your Machine Learning Project

Technical Deep-Dive: ML Project With Causal Inference

Prompt

What to cover (3–5 minutes, then be ready to dive deeper)

Solution

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