diff --git a/6 Causal Inference/6-2 Randomized Experiments/Randomized-Experiments-Solutions.html b/6 Causal Inference/6-2 Randomized Experiments/Randomized-Experiments-Solutions.html index 0115c8a..ce10d3d 100644 --- a/6 Causal Inference/6-2 Randomized Experiments/Randomized-Experiments-Solutions.html +++ b/6 Causal Inference/6-2 Randomized Experiments/Randomized-Experiments-Solutions.html @@ -23,7 +23,7 @@ - + @@ -3026,7 +3026,7 @@

In this lab, we are going to discuss Randomized Experiments. Causal inference methods can be used for observational data, but it is easier to first consider them in the context of randomized experiments. To begin we are going to created simulated data that we’d be unlikely to encounter in the real world where we give the same individual the treatment and then NOT give them a treatment. We’ll then calculate the “true” *Average Treatment Effect (ATE) and then show how different techniques of applying randomization will give us very close.

We will be leaning heavily on the dplyr library, so I’d encourage you to refer the dplyr cheat sheet to refresh your memory and push your knowledge of how to use the library.

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1.1 Causal Typesp_4 <- df_freq$prop[4] # immune

This table shows us how many individuals in our population of interest had each of four possible sets of outcomes with and without the use of AspiTyleCedrin, which we may refer to as four different causal “types”:

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2.1 Individual-level Treatment Ef

2.2 Average Treatment Effect (ATE)

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So, a common causal parameter of interest is the *Average Treatement Effect (ATE), which is the average difference in the pair of potential outcomes averaged over the entire population of interest (at a particular moment in time). In other words, it is just the average (or expected value) of the individual-level treatment effect.

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So, a common causal parameter of interest is the *Average Treatment Effect (ATE), which is the average difference in the pair of potential outcomes averaged over the entire population of interest (at a particular moment in time). In other words, it is just the average (or expected value) of the individual-level treatment effect.

\[\text{ATE} = E[Y_{i1} - Y_{i0}]\] QUESTION 2: Use the ITE column you just added to df to find the average treatment effect of AspiTyleCedrin on migraines in this population and assign it to the variable name ATE.

# calculate average treatment effect for across our sample