Learn the Basics of Causal Inference with R
Why Learn Linear Regression in R?
You hear it all the time: “correlation is not causation.” By taking this course, you’ll find out what causation really is. Find out why things happen using causal methods, such as matching and weighting, instrumental variables, and difference in differences.
In this course, you will learn the conceptual foundations for determining causal inference and how to work with data to understand why things happen. In addition to the basic foundations, you will learn how to isolate variables and apply different techniques to deal with unruly datasets and interpret the results of your analysis.
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- 4Learn about regression discontinuity design and instrumental variables for causal inference.
Impact of Cover Crops on Wheat Crop Yields
In this project, you will use inverse probability of treatment weighting (IPTW) to estimate the causal effect of cover crop usage on wheat crop yields.
Effect of Emergency Weather Systems on Transit Times
Apply regression discontinuity design (RDD) to estimate the causal effect of a snow emergency system on average commuting time in a fictitious city.
Enhanced Recovery After Surgery
Analyze the effect of a surgical recovery plan on patient outcomes using the Difference in Differences (DID) technique in R.
— Madelyn, Pinterest
I know from first-hand experience that you can go in knowing zero, nothing, and just get a grasp on everything as you go and start building right away.
Find out when correlation really is causation!
DetailsEarn a certificate of completion
7 hours to complete in total