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‎slides/00-intro.html‎

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# Causal Inference in R: Introduction
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# Causal Modeling in R: Whole Game
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‎slides/02-dags.html‎

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# Causal Diagrams in R
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### We're going to assume that coffee does not cause cancer, so there's no formula for that. But we still need to declare our causal question. Specify "coffee" as the exposure and "cancer" as the outcome (both in quotations marks).
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### Plot the DAG using `ggdag()`
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### Call `tidy_dagitty()` on `coffee_cancer_dag` to create a tidy DAG, then pass the results to `dag_paths()`. What's different about these data?
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### Plot the open paths with `ggdag_paths()`. (Just give it `coffee_cancer_dag` rather than using `dag_paths()`; the quick plot function will do that for you.) Remember, since we assume there is *no* causal path from coffee to lung cancer, any open paths must be confounding pathways.
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#### Use `ggdag_adjustment_set()` to visualize the adjustment sets. Add the arguments `use_labels = "label"` and `text = FALSE`.
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#### Write an R formula for each adjustment set, as you might if you were fitting a model in `lm()` or `glm()`
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‎slides/03-pscores.html‎

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<title>Propensity Scores</title>
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<meta name="author" content="Lucy D’Agostino McGowan" />
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# Propensity Scores
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### Lucy D’Agostino McGowan
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### Wake Forest University
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## Your turn
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