Measure Speaker's Impact Using Propensity Score Matching
Causal Impact of Optional Device Ownership on Engagement (Observational Design)
Scenario
A new, optional smart speaker is released. Users can choose to purchase and link the speaker to their account, while others do not. Due to the nature of user self-selection, a forced A/B test cannot be conducted.
Assume the following data is available:
- User-level panel data (daily/weekly) on engagement metrics (e.g., streaming hours, sessions).
- A time-stamped indicator for speaker activation/ownership for each user.
- Rich covariates, including prior engagement levels and trends, demographics, geographical information, other owned devices, and marketing exposure.
- Staggered adoption timing across users, meaning users activate the speaker at different points in time.
Task
Describe how you would measure the causal impact of owning the smart speaker on user engagement. Your answer should detail your preferred design and provide justification.
Specifically, address the following points:
- Identification Strategy and Main Assumptions: Outline your chosen strategy for identifying causal effects and state the core assumptions required for its validity.
- Control Group Construction and Staggered Adoption: Explain how you would construct an appropriate control group and effectively handle the staggered timing of speaker adoption among users.
- Model/Estimator Choice and Diagnostics: Specify the statistical model or estimator you would use, along with the diagnostic checks (e.g., pre-trend checks) necessary to validate your approach.
- Sensitivity Analyses and Alternatives: Discuss how you would perform sensitivity analyses to test the robustness of your findings and what alternative approaches you would consider if your primary assumptions are violated.
You may consider quasi-experimental approaches such as difference-in-differences (event study), propensity-score matching/weighting, instrumental variables, and pre-post checks.
AI interview prep
Core dilemma
Establishing a robust causal link between an optional, self-selected action (speaker ownership) and an outcome (engagement) in an observational setting, while effectively mitigating self-selection bias, confounding variables, and the complexities introduced by staggered adoption.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Measure Speaker's Impact Using Propensity Score Matching
Causal Impact of Optional Device Ownership on Engagement (Observational Design)
Scenario
A new, optional smart speaker is released. Users can choose to purchase and link the speaker to their account, while others do not. Due to the nature of user self-selection, a forced A/B test cannot be conducted.
Assume the following data is available:
- User-level panel data (daily/weekly) on engagement metrics (e.g., streaming hours, sessions).
- A time-stamped indicator for speaker activation/ownership for each user.
- Rich covariates, including prior engagement levels and trends, demographics, geographical information, other owned devices, and marketing exposure.
- Staggered adoption timing across users, meaning users activate the speaker at different points in time.
Task
Describe how you would measure the causal impact of owning the smart speaker on user engagement. Your answer should detail your preferred design and provide justification.
Specifically, address the following points:
- Identification Strategy and Main Assumptions: Outline your chosen strategy for identifying causal effects and state the core assumptions required for its validity.
- Control Group Construction and Staggered Adoption: Explain how you would construct an appropriate control group and effectively handle the staggered timing of speaker adoption among users.
- Model/Estimator Choice and Diagnostics: Specify the statistical model or estimator you would use, along with the diagnostic checks (e.g., pre-trend checks) necessary to validate your approach.
- Sensitivity Analyses and Alternatives: Discuss how you would perform sensitivity analyses to test the robustness of your findings and what alternative approaches you would consider if your primary assumptions are violated.
You may consider quasi-experimental approaches such as difference-in-differences (event study), propensity-score matching/weighting, instrumental variables, and pre-post checks.