{"blocks": [{"key": "11c7773c", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "bc87bc9a", "text": "Product metrics deep-dive and causal inference discussion with a manager concerned about growth and experience quality.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "b8904c45", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2444d1fd", "text": "a) You notice a steady decline in daily active users over several weeks. What metrics would you define and what experiment or analysis would you design to diagnose the issue?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "83b56c10", "text": "", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "84328a55", "text": "b) A manager wants to quantify how users’ network speed affects TikTok usage, but only observational data are available. Describe a suitable causal-inference approach and how you would explain its validity to a non-technical stakeholder.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "1aa3d567", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "c5b1533b", "text": "Think retention, cohorts, segmentation, IV / propensity weighting, clearly communicate assumptions.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}