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  3. Causal Inference with R - Experiments

Exercise

Putting it All Together with KittyCatch: Part 4 - Missing Data Problems

Let's continue our look at our data by checking for balance on our treatment variable and outcome variable, and looking to see how correlated they are. One common problem in data science is missing data: empty gaps in the values of some variables that will throw off our results. If we find any in our key variables, we will need to deal with them.

Instructions

100 XP
  • 1) Check for balance on TotalHoursPlayed
  • 2) See the correlation between the number of hours played and distance walked.
  • 3) Look at the average values of TotalHoursPlayed in the treatment and control groups.
  • 4) Is DistanceWalked balanced across treatment and control groups?
  • 5) Use a rough method to remove some problem data entries