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Core
Causal Inference: Confounders and Identification
Causal inference is the discipline of estimating what would happen if you intervened, not just what correlates in observed data. It centers on confounders, randomization as the gold standard, and quasi-experimental methods (diff-in-diff, instrumental variables, propensity scores) for when you cannot run a clean experiment. Applied AI interviews probe it because product and model decisions hinge on whether a measured lift is real or an artifact of who self-selected into the treatment.
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Machine Learning & Data ScienceHow do you measure impact when you can't run a clean A/B test (difference-in-differences, synthetic control, IV)?→Machine Learning & Data ScienceDesign an A/B test for a model change: power, sample size, significance, and the peeking problem.→Machine Learning & Data ScienceYour A/B test shows the control and treatment groups differ before the treatment even applies. What's wrong?→Machine Learning & Data ScienceWalk me through instrumental variables: what makes an instrument valid, and how do two-stage least squares and LATE work?→Behavioral & Project Deep-DivesTell me about an experiment or project that failed, or a time you changed your mind based on data.→MLOps & ML EngineeringDesign an online experimentation (A/B testing) platform for ML models at scale.→
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