Understand Propensity Score Matching in Business Analytics

Q: Understand Propensity Score Matching in Business Analytics

This question evaluates understanding of propensity score matching as a causal inference technique and related competencies in estimating treatment effects from observational product data, assessing required assumptions for unbiased estimates, and measuring covariate balance; the domain tested is Statistics & Math for a Data Scientist role.

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Question

Propensity Score Matching (PSM) in Observational Product Data

Context

You work on a growth analytics team estimating causal effects (e.g., of a feature rollout or marketing campaign) using observational product data where randomized experiments are not available.

Questions

What is Propensity Score Matching (PSM) and in which business situations would you apply it?
List the assumptions required for PSM to yield unbiased causal estimates.
How do you assess whether the matching achieved good covariate balance?

Understand Propensity Score Matching in Business Analytics

Propensity Score Matching (PSM) in Observational Product Data

Context

Questions

Solution

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