{
  "id": 28147,
  "title": "Have the winners published their method publicly?",
  "url": "/competitions/cause-effect-pairs/discussion/28147",
  "author_name": "",
  "post_date": "2017-01-26T16:35:05.487Z",
  "votes": null,
  "comment_count": 1,
  "views": 21,
  "content": "<p>Hi,</p>\n\n<p>I would like to test some methods on my own data and wonder if someone has published a working (preferably) python code. In my data one parameter is discrete binary ($y={0,1}$) and there are multiple other feature variables $x_i$ some of which discrete and some are continuous. I would like to know what $x_i$ caused $y$ and what $x$ are consequence of $y$.</p>\n\n<p>Best,\nHanan</p>",
  "messages": [
    {
      "id": "158281",
      "postDate": "01/26/2017 16:35:05",
      "content": "<p>Hi,</p>\n\n<p>I would like to test some methods on my own data and wonder if someone has published a working (preferably) python code. In my data one parameter is discrete binary ($y={0,1}$) and there are multiple other feature variables $x_i$ some of which discrete and some are continuous. I would like to know what $x_i$ caused $y$ and what $x$ are consequence of $y$.</p>\n\n<p>Best,\nHanan</p>",
      "rawMarkdown": "Hi,\r\n\r\nI would like to test some methods on my own data and wonder if someone has published a working (preferably) python code. In my data one parameter is discrete binary ($y={0,1}$) and there are multiple other feature variables $x_i$ some of which discrete and some are continuous. I would like to know what $x_i$ caused $y$ and what $x$ are consequence of $y$.\r\n\r\nBest,\r\nHanan",
      "votes": null
    },
    {
      "id": "158296",
      "postDate": "01/26/2017 17:42:45",
      "content": "<p>Yes. <a href=\"https://www.kaggle.com/c/cause-effect-pairs/forums/t/5702/code-of-top-ranking-participants\">https://www.kaggle.com/c/cause-effect-pairs/forums/t/5702/code-of-top-ranking-participants</a></p>",
      "rawMarkdown": "Yes. https://www.kaggle.com/c/cause-effect-pairs/forums/t/5702/code-of-top-ranking-participants",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 158296,
      "author_name": "jarfo1",
      "author_url": "",
      "post_date": "01/26/2017 17:42:45",
      "content": "<p>Yes. <a href=\"https://www.kaggle.com/c/cause-effect-pairs/forums/t/5702/code-of-top-ranking-participants\">https://www.kaggle.com/c/cause-effect-pairs/forums/t/5702/code-of-top-ranking-participants</a></p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "158281": "Hi,\r\n\r\nI would like to test some methods on my own data and wonder if someone has published a working (preferably) python code. In my data one parameter is discrete binary ($y={0,1}$) and there are multiple other feature variables $x_i$ some of which discrete and some are continuous. I would like to know what $x_i$ caused $y$ and what $x$ are consequence of $y$.\r\n\r\nBest,\r\nHanan",
    "158296": "Yes. https://www.kaggle.com/c/cause-effect-pairs/forums/t/5702/code-of-top-ranking-participants"
  },
  "source": "meta"
}