Measure speaker impact without A/B testing
Problem Statement
Your company launched an optional smart speaker on January 10, 2025. Adoption is voluntary, with approximately 6% of existing app users purchasing the device within 60 days of launch. You are explicitly prohibited from forcing adoption or running an A/B test to measure its impact.
Available Data
You have access to a rich dataset including:
- User-level app logs
- Device graph
- CRM attributes
- Ad exposures
- Product prices
- Shipment data
- Information on regional stockouts
Deliverables
Your task is to design a comprehensive approach to estimate the causal effect of smart speaker adoption on user engagement.
Part A: Engagement Metric & Guardrails
Define and justify one primary engagement metric. Additionally, propose guardrail metrics to monitor for unintended side effects (e.g., user churn, substitution of usage to other devices).
Part B: Identification Strategies
Design two complementary identification strategies to estimate the causal effect on engagement at both +30 days and +180 days post-adoption. For each strategy, you must:
- Strategy 1: Utilize a staggered-adoption Difference-in-Differences (DiD) or Event-Study approach.
- Strategy 2: Employ a Propensity-Score Matched Inverse Probability Weighting (IPW), Doubly Robust (DR) estimation, or a Synthetic Control method, applied at either the user or region level.
For both strategies, clearly articulate:
- The estimand (the specific causal effect you aim to measure).
- Key assumptions underlying the method.
- Diagnostics you would perform (e.g., pre-trends analysis, covariate balance checks, negative controls).
- How you would handle potential interference, such as household spillovers.
- How you would account for seasonality in user behavior.
Part C: Instrumental Variable Approach
Leverage exogenous variation from regional stockouts and shipping delays as an instrumental variable (IV). For this approach, you must:
- Argue for the relevance and exclusion criteria of your chosen instrument.
- Outline specific falsification tests to validate the instrument's assumptions.
Part D: Power Analysis
Given a 6% adoption rate and historical variance in engagement, provide a back-of-the-envelope power analysis. Specifically, determine the sample size or observation horizon needed to detect a 3% lift in your primary engagement metric.
AI interview prep
Core dilemma
Estimating a robust causal effect from observational data in the absence of A/B testing, specifically dealing with selection bias, confounding variables, and interference, while ensuring the proposed analytical system is scalable and resilient to data quality issues.
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 impact without A/B testing
Problem Statement
Your company launched an optional smart speaker on January 10, 2025. Adoption is voluntary, with approximately 6% of existing app users purchasing the device within 60 days of launch. You are explicitly prohibited from forcing adoption or running an A/B test to measure its impact.
Available Data
You have access to a rich dataset including:
- User-level app logs
- Device graph
- CRM attributes
- Ad exposures
- Product prices
- Shipment data
- Information on regional stockouts
Deliverables
Your task is to design a comprehensive approach to estimate the causal effect of smart speaker adoption on user engagement.
Part A: Engagement Metric & Guardrails
Define and justify one primary engagement metric. Additionally, propose guardrail metrics to monitor for unintended side effects (e.g., user churn, substitution of usage to other devices).
Part B: Identification Strategies
Design two complementary identification strategies to estimate the causal effect on engagement at both +30 days and +180 days post-adoption. For each strategy, you must:
- Strategy 1: Utilize a staggered-adoption Difference-in-Differences (DiD) or Event-Study approach.
- Strategy 2: Employ a Propensity-Score Matched Inverse Probability Weighting (IPW), Doubly Robust (DR) estimation, or a Synthetic Control method, applied at either the user or region level.
For both strategies, clearly articulate:
- The estimand (the specific causal effect you aim to measure).
- Key assumptions underlying the method.
- Diagnostics you would perform (e.g., pre-trends analysis, covariate balance checks, negative controls).
- How you would handle potential interference, such as household spillovers.
- How you would account for seasonality in user behavior.
Part C: Instrumental Variable Approach
Leverage exogenous variation from regional stockouts and shipping delays as an instrumental variable (IV). For this approach, you must:
- Argue for the relevance and exclusion criteria of your chosen instrument.
- Outline specific falsification tests to validate the instrument's assumptions.
Part D: Power Analysis
Given a 6% adoption rate and historical variance in engagement, provide a back-of-the-envelope power analysis. Specifically, determine the sample size or observation horizon needed to detect a 3% lift in your primary engagement metric.