What's the biggest challenge in data science? Causal Inference

This was the top requested topic based on a poll I posted, especially from fellow Data Scientists. What is the biggest challenge in data science today? There are many great challenges data scientists face in an everchanging world, and one that is growing increasingly specialized in new areas of expertise. But the biggest problem I see is Causal Inference. It’s a topic with limited expert materials, unsatisfying solutions, high demand but a low supply of people capable of creating effective solutions, and a massive blindness to the fact that this is the problem many are trying to answer. I cannot think of any area of data science that faces all of those obstacles.

Hopefully this video will get you more familiar with causal inference and the problem it poses for data science, and inspire you to learn more about this topic so you can fill the gap that exists today.

Why is it the biggest challenge: 0:44

Where can you learn more: 9:57

Susan Athey video lecture - https://youtu.be/yKs6msnw9m8

Recommendations: 10:30

Refutation tests: 12:36

Study the variable types discussed in the video - https://significantlystatistical.wordpress.com/2014/12/12/confounders-mediators-moderators-and-covariates/

Disclaimer: this video is my own and does not necessarily reflect the positions, strategies, or opinions of 84.51° or our parent company, The Kroger Co.