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causal-powers
Superpowers for data analytics, causal inference, and econometrics: discipline skills that make the silent failures of data work loud — data contracts, data preparation, join-cardinality checks, wrong-number debugging, result verification, pre-analysis plans, descriptive evidence (stylized facts, trends, summary-stats tables, distributions, and maps done honestly — composition checks, real-vs-nominal, rate-not-count), causal identification, structural estimation (demand/BLP, dynamic discrete choice, games, auctions, consideration, search), predictive modeling (applied prediction — leakage-safe evaluation, deployment-matched splits, prediction-is-not-causation), analysis craft, human-in-the-loop checkpoints, plan execution with parallel subagents, research-project organization, and a configurable R-first language profile. R, Julia, and Python.
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