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Causal Inference with R - Introduction

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In solidarity with the DataCamp instructors who are protesting the recent corporate response to behavior of a member of its executive staff, we have suspended this course. If and when DataCamp has made a satisfactory response to this situation, we will make the course available again. We still have materials about causal inference at our Mod-U website (modu.ssri.duke.edu). To learn more about the events at DataCamp, you can read the following letter signed by 100 DataCamp instructors -docs.google.com/document/d/1gWqAlf7yhDBCZhkbLUysOCmMl58IrVdG26C8Hiy9b-I

Course Suspended

In solidarity with the DataCamp instructors who are protesting the recent corporate response to behavior of a member of its executive staff, we have suspended this course. If and when DataCamp has made a satisfactory response to this situation, we will make the course available again. We still have materials about causal inference at our Mod-U website (modu.ssri.duke.edu). To learn more about the events at DataCamp, you can read the following letter signed by 100 DataCamp instructors - docs.google.com/document/d/1gWqAlf7yhDBCZhkbLUysOCmMl58IrVdG26C8Hiy9b-I

Introduction to Treatment Effects

This chapter will introduce you to individual, group, and average treatment effects, and will let you learn and practice through R

Confounders, Counterfactuals, and p-Hacking

This chapter will introduce you the important issues of confounders, counterfactuals, and the problem of p-hacking, and will let you learn and practice through R