causal-powers

{
  "name": "causal-powers",
  "description": "Causal Powers \u2014 a skills library for data analytics, causal inference, and reduced-form and structural econometrics.",
  "owner": {
    "name": "Lance Gui",
    "email": "[email protected]"
  },
  "plugins": [
    {
      "name": "causal-powers",
      "description": "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.",
      "version": "0.29.1",
      "source": "./",
      "author": {
        "name": "Lance Gui",
        "email": "[email protected]"
      }
    }
  ]
}