A Crash Course on Causality – Part 2
A guide to building robust decision-making systems in businesses with causal inference.
Today, I am continuing the two-article series on causality (or causal inference).
I published the first part earlier this week.
The second part is available as a deep dive here: A Crash Course on Causality – Part 2.
Why care?
“Because” is possibly one of the most powerful words in business decision-making.
“Our customer satisfaction improved because we introduced personalized recommendations.”
“The energy consumption dropped because of the new efficiency standards implemented.”
Backing any observation/insights with causality gives so much ability to confidently use the word “because” in business/regular discussions.
Identifying these causal relationships is vital because these relationships typically require an additional inspection and statistical analysis that goes beyond the typical correlation analysis (which anyone can do).
Thus, in this two-part article series, we shall:
dive into the details of causality
why it is difficult
what is counterfactual learning
four common techniques to determine causal impacts.
learn some of the most widely used techniques in causal inference.
and more about my personal experience using it in my projects.
Hoping you aspire to make valuable contributions to your data science job; this series will be super helpful in cultivating a diversified skill set.
Read the part 1 here: A Crash Course on Causality – Part 1.
Read the part 2 here: A Crash Course on Causality – Part 2
Have a good day!
Avi
