Diagnose and fix selection bias in experiments
Problem: Diagnosing and Correcting Selection Bias in Opt-in Experiments
Context
You are observing user behavior where individuals have the option to "opt-in" to view a discount banner. If a user opts in and subsequently sees the banner, they are considered part of the "treated" group (treatment = 1). Initial observational analysis reveals that users who opted in and saw the banner exhibit a higher conversion rate compared to users who did not.
Tasks
Part (a): Understanding Selection Bias
- Define selection bias in general terms.
- For this specific opt-in scenario, identify and describe at least three plausible mechanisms that could lead to selection bias.
- For each identified mechanism, explain the expected direction of bias it would induce on a naive comparison of conversion rates between treated and untreated users.
Part (b): Proposing Unbiased Causal Estimation Methods
Propose two distinct study designs or estimation techniques capable of yielding unbiased, or at least less biased, causal estimates of the discount banner's effect on conversion in this opt-in setting. For each proposed method, you must clearly articulate:
- Its specific setup or design.
- The fundamental identifying assumptions required for its validity.
- Practical strategies or tests you would employ to diagnose or verify these identifying assumptions.
- Examples of suitable methods include, but are not limited to: Randomized Encouragement with an Instrumental Variable (IV), Regression Discontinuity with thresholded eligibility, Front-Door Adjustment, or IV with an assignment instrument.
Part (c): Numerical Illustration
Construct a concise numerical example where the simple difference in conversion rates between treated and untreated groups is demonstrably biased, but one of your proposed corrected methods successfully recovers an unbiased or less biased estimate of the true causal effect.
AI interview prep
Core dilemma
The central engineering conflict is to accurately estimate the true causal impact of an 'opt-in' discount banner on user conversion, despite the inherent self-selection bias in observational data. This requires disentangling the banner's effect from pre-existing differences between users who choose to engage with it and those who do not.
3 tradeoffs and 2 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Diagnose and fix selection bias in experiments
Problem: Diagnosing and Correcting Selection Bias in Opt-in Experiments
Context
You are observing user behavior where individuals have the option to "opt-in" to view a discount banner. If a user opts in and subsequently sees the banner, they are considered part of the "treated" group (treatment = 1). Initial observational analysis reveals that users who opted in and saw the banner exhibit a higher conversion rate compared to users who did not.
Tasks
Part (a): Understanding Selection Bias
- Define selection bias in general terms.
- For this specific opt-in scenario, identify and describe at least three plausible mechanisms that could lead to selection bias.
- For each identified mechanism, explain the expected direction of bias it would induce on a naive comparison of conversion rates between treated and untreated users.
Part (b): Proposing Unbiased Causal Estimation Methods
Propose two distinct study designs or estimation techniques capable of yielding unbiased, or at least less biased, causal estimates of the discount banner's effect on conversion in this opt-in setting. For each proposed method, you must clearly articulate:
- Its specific setup or design.
- The fundamental identifying assumptions required for its validity.
- Practical strategies or tests you would employ to diagnose or verify these identifying assumptions.
- Examples of suitable methods include, but are not limited to: Randomized Encouragement with an Instrumental Variable (IV), Regression Discontinuity with thresholded eligibility, Front-Door Adjustment, or IV with an assignment instrument.
Part (c): Numerical Illustration
Construct a concise numerical example where the simple difference in conversion rates between treated and untreated groups is demonstrably biased, but one of your proposed corrected methods successfully recovers an unbiased or less biased estimate of the true causal effect.