Merge pull request #81 from r-causal/metadata

add profiles, readme and re-render

Author: malcolmbarrett

_freeze/slides/raw/00-intro/execute-results/html.json

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+    "markdown": "---\ntitle: \"Causal Inference in R: Introduction\"\nformat: \"kakashi-revealjs\"\n---\n\n## `> who_are_we(c(\"lucy\", \"malcolm\", \"travis\"))`\n\n\n\n\n::: {.cell layout-align=\"center\"}\n<style type=\"text/css\">\nimg {\n  height: 250px;\n  width: 250px;\n  border-radius: 50%;\n}\n</style>\n:::\n\n\n:::: {.columns}\n\n::: {.column width=\"33.3%\"}\n<br />\n<br />\n\n<img src=\"img/ldm.jpg\" class=\"rounded-circle\" />\n\n::: {.small}\n<br />\n<br />\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [https://www.lucymcgowan.com/](https://www.lucymcgowan.com/)\n:::\n:::\n\n::: {.column width=\"33.3%\"}\n<br />\n<br />\n\n::: {.small}\n<img src=\"img/mb.jpg\" class=\"rounded-circle\" />\n<br />\n&nbsp;&nbsp;&nbsp; [https://www.malco.io/](https://www.malco.io/)\n:::\n:::\n\n\n::: {.column width=\"33.3%\"}\n<br />\n<br />\n<img src=\"img/tg.jpg\" class=\"rounded-circle\" />\n\n\n\n::: {.small}\n<br />\n<br />\n<br />\n[https://travisgerke.com/](https://travisgerke.com/)\n:::\n:::\n\n::::\n\n\n## The three practices of analysis {background-color=\"#23373B\"}\n\n1. Describe\n2. Predict\n3. Explain\n\n## {background-color=\"#23373B\" .center}\n### Normal regression estimates associations. But we want *counterfactual, causal* estimates: \n<br />\n\n### What would happen if *everyone* in the study were exposed to x vs if *no one* was exposed.\n\n\n## {background-color=\"#23373B\" .center}\n### For causal inference, we need to make sometimes unverifiable assumptions. \n\n<br />\n\n### Today, we'll focus on the assumption of *no confounding*.\n\n## Tools for causal inference {background-color=\"#23373B\"}\n\n- Causal diagrams\n- Propensity score weighting\n- Propensity score matching\n- G-methods & friends\n\n## Other tools for causal inference {background-color=\"#23373B\"}\n\n- Randomized trials\n- Instrumental variables & friends\n- TMLE and other ML for causal inference\n\n## Resources {background-color=\"#23373B\"}\n### [Causal Inference in R](https://www.r-causal.org/): Our book! Free online.\n### [Causal Inference](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/): Comprehensive text on causal inference. Free online.\n### [The Book of Why](http://bayes.cs.ucla.edu/WHY/): Detailed, friendly intro to DAGs and causal inference. \n### [Mastering 'Metrics](http://www.masteringmetrics.com/): Friendly introduction to IV-based methods\n",
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_freeze/slides/raw/00-intro/libs/clipboard/clipboard.min.js

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