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_site/robots.txt

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+Sitemap: https://r-causal.github.io/causal_workshop_website/sitemap.xml

_site/search.json

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     "section": "",
     "text": "Welcome to the Causal Inference in R Workshop!"
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-    "title": "About",
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-    "text": "About this site\n\n1 + 1\n\n[1] 2"
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     "section": "Audience",
     "text": "Audience\nThis course is for you if you:\n\nknow how to fit a linear regression model in R,\nhave a basic understanding of data manipulation and visualization using tidyverse tools, and\nare interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships."
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+    "title": "Causal Inference in R Workshop",
+    "section": "Topics",
+    "text": "Topics\nWe offer this workshop in one-day and two-day formats.\nIn the one-day format, we cover:\n\nExamples of the causal inference workflow\nWhen we can use standard methods and when we should use specialized causal methods\nSpecifying causal questions as Directed Acyclic Graphs (DAGs)\nFitting, diagnosing, and applying propensity score models using weighting and matching\n\nIn the two-day format, we cover the above plus these additional topics:\n\nSensitivity analysis\nG-computation\nContinuous exposures with g-computation\nAnd more worked examples\n\nSometimes we have extra time, and so we also have a couple of bonus topics:\n\nContinuous exposures with propensity scores\nSelection bias"
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     "objectID": "setup.html",
     "href": "setup.html",
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     "section": "Hosted slides",
     "text": "Hosted slides\nFollowing the above instructions will download a PDF copy of the slides. If you’d like to see the HTML version of the slides, we recommend taking a look at the hosted versions rather than the raw versions in the repository.\n\nSlides\n\n00 Intro\n01 Whole Game\n02 When Standard Methods Succeed\n03 Causal Inference with group_by and summarise\n04 Causal Diagrams\n05 Causal Inference is Not Just a Statistics Problem\n06 Introduction to Propensity Scores\n07 Using Propensity Scores\n08 Checking Propensity Scores\n09 Fitting the outcome model\n10 Continuous Exposures and G-Computation\n11 Tipping Point Sensitivity Analyses\n12 Whole Game (Your Turn)\n13 Bonus: Selection Bias\n14 Bonus: Continous Exposures with Propensity Scores"
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-    "title": "Causal Inference in R Workshop",
-    "section": "Topics",
-    "text": "Topics\nWe offer this workshop in one-day and two-day formats.\nIn the one-day format, we cover:\n\nExamples of the causal inference workflow\nWhen we can use standard methods and when we should use specialized causal methods\nSpecifying causal questions as Directed Acyclic Graphs (DAGs)\nFitting, diagnosing, and applying propensity score models using weighting and matching\n\nIn the two-day format, we cover the above plus these additional topics:\n\nSensitivity analysis\nG-computation\nContinuous exposures with g-computation\nAnd more worked examples\n\nSometimes we have extra time, and so we also have a couple of bonus topics:\n\nContinuous exposures with propensity scores\nSelection bias"
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     "text": "The best complement to the workshop is our book, so make sure to check it out!\n\nFurther reading\n\nOther books\nThere are also several other excellent books on causal inference. Our book is different in its focus on R, but it’s still helpful to see this area from other perspectives. A few books you might like:\n\nCausal Inference: What If?\nCausal Inference: The Mixtape\nThe Effect\n\nThe first book is focused on epidemiology. The latter two are focused on econometrics. We also recommend The Book of Why for more on causal diagrams.\n\n\nInteresting papers\nHere are some interesting papers we commonly mention in the workshop or related to key topics:\n\nA Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks: A musing on the tasks of data science (description, prediction, and causal inference)\nTo Explain or Predict: A detailed analysis of the differences between causal and predictive modeling.\nChoosing the Causal Estimand for Propensity Score Analysis of Observational Studies: A discussion of when to use different estimands. Includes a helpful table with a summary.\nTo Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and Butterfly-Bias: A simulation study showing that for m-bias, confounding is usually a bigger issue than collider bias.\n\nEffects of Adjusting for Instrumental Variables on Bias and Precision of Effect Estimates: A simulation study that shows, for many analyses, the risk of confounding is greater than the risk of bias from adjusting for an instrumental variable."
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-    "title": "Setup and Materials",
-    "section": "Hosted slides",
-    "text": "Hosted slides\nFollowing the above instructions will download a PDF copy of the slides. If you’d like to see the HTML version of the slides, we recommend taking a look at the hosted versions rather than the raw versions in the repository.\n\nSlides\n\n00 Intro\n01 Whole Game\n02 When Standard Methods Succeed\n03 Causal Inference with group_by and summarise\n04 Causal Diagrams\n05 Causal Inference is Not Just a Statistics Problem\n06 Introduction to Propensity Scores\n07 Using Propensity Scores\n08 Checking Propensity Scores\n09 Fitting the outcome model\n10 Continuous Exposures and G-Computation\n11 Tipping Point Sensitivity Analyses\n12 Whole Game (Your Turn)\n13 Bonus: Selection Bias\n14 Bonus: Continous Exposures with Propensity Scores"
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