GeezerVison

📐 Causal inference and econometrics: Smoothing event-study estimates, Low-rank generalized least squares, Monte Carlo tests for estimators, etc.

🧪 ML predictions as measurements: Classifier error in exposure comparisons, Label audits for machine-scored experiments, etc.

📋 Data collection: Efficient labeling for share regressions, Optimal survey sample allocation, etc.

🎯 Calibration and cutoffs: Probability calibration with fewer ties, Streaming survey weights, Optimal classification thresholds, etc.

⚓ Stability: Auditing prediction instability, Bootstrap consistency training, Reducing forgetting during retraining, etc.

🧭 Less greedy methods: Decision trees from recurring splits, etc.

🔎 Joins: High-precision record linkage, Record linkage that preserves groups, Randomization inference with uncertain linkage

〰️ Smoothing, binning, and dimension reduction: Local trends in noisy time series, Kernel bandwidth selection, Dimension reduction by projection pursuit, etc.

💬 Measuring political knowledge and learning: Learning estimates adjusted for guessing, Hidden political knowledge?, Survey design and partisan knowledge gaps, etc.

🗳️ Measuring opinion and deliberation: Who has unstable political attitudes?, Auditing deliberative polls, Measuring support for militant groups, etc.

⚖️ Benchmarks: Designing and auditing ML benchmarks, AI benchmark for econometrics

📚 Other: R statistical tools for AI assistants, Learning from data, Discriminant adaptive nearest neighbors, etc.

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