Question 1

What is the difference-in-differences technique?

Accepted Answer

Correlation isn’t causation, and it’s not enough to say that two things are related. We have to show proof, and the difference-in-differences technique is a causal inference method we can use to prove (as much as possible) that one thing causes another.

Question 2

How is the difference-in-differences technique used in programming?

Accepted Answer

There are many situations where you can use the difference-in-differences (DiD) technique to help make sense of your data, but implementing it can be complex, so it’s important to know when to use it and how it’ll affect your results (which you’ll learn in this course).

Question 3

What kind of jobs use the difference-in-differences technique?

Accepted Answer

Data Scientists need to verify their hypotheses, and when they don’t have the time (or money) to conduct an A/B test, causal inference tools like the difference-in-differences technique can be helpful; especially for those who work in the economic, social, health, and government industries.

Question 4

What else should I study if I am learning about causal inference?

Accepted Answer

Along with the difference-in-difference technique, you’ll also want to explore other basic causal methods like matching, weighting, propensity scoring, instrumental variables, and regression discontinuity. And if you want to do more with statistics, the R programming language was designed with statistics in mind, and many of its packages help you make formulas, functions, and visualizations easier.

Difference in Differences for Causal Inference

About this course

Syllabus