{
  "id": "1806.01488",
  "version": "v1",
  "published": "2018-06-05T04:19:22.000Z",
  "updated": "2018-06-05T04:19:22.000Z",
  "title": "A Primer on Causal Analysis",
  "authors": [
    "Finnian Lattimore",
    "Cheng Soon Ong"
  ],
  "comment": "Parts of this document are copied verbatim from Finnian Lattimore's PhD thesis, ANU 2018",
  "categories": [
    "cs.LG",
    "cs.AI",
    "stat.ML"
  ],
  "abstract": "We provide a conceptual map to navigate causal analysis problems. Focusing on the case of discrete random variables, we consider the case of causal effect estimation from observational data. The presented approaches apply also to continuous variables, but the issue of estimation becomes more complex. We then introduce the four schools of thought for causal analysis",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-06-05T04:19:22.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "navigate causal analysis problems",
      "discrete random variables",
      "causal effect estimation",
      "conceptual map",
      "observational data"
    ],
    "tags": [
      "dissertation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}