{"blocks": [{"key": "0487635e", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "3fec813c", "text": "A mobile app is redesigned, but the new version is only adopted by users who choose to upgrade; the team needs to measure performance impact.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "e7c8ab2e", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "b54c0037", "text": "Without a forced A/B test, how would you estimate the causal impact of the redesign? How would you define comparable treatment and control groups of ‘new-version’ and ‘old-version’ users? Which user features beyond engagement/behavior would you include to ensure similarity? What statistical or causal-inference methods would you apply, and how would you validate assumptions?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "2d2270ba", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "7d8cc04e", "text": "Discuss propensity scores, matching, weighting, diff-in-diff, covariate balance checks, sensitivity analyses.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}