{"blocks": [{"key": "37a42da3", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "a6c24163", "text": "Several experimentation and product-metric challenges for a social-media homepage.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "afcab658", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "723ebf9f", "text": "An intern launched an experiment without a control group—how can you still estimate treatment impact? Compare matching versus propensity-score weighting and discuss their trade-offs. Review an existing A/B test and list common pitfalls that could bias the results. For a new horizontal home-feed module, what primary metrics would you track to judge success? Post-launch you observe homepage click-through dropping in treatment while DAU and time-spent stay flat—how would you investigate root causes and which user segments would you analyze first?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "0981da3a", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "6a461237", "text": "Think causal inference, experiment design, diagnosable metrics hierarchy, segmentation by device, geography, tenure, power-users vs casual, etc.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}