How data scientists approach causal inference

In a recent Creator Highlight Q&A, Matteo Courthoud reflected on the growing importance of constructing robust predictions, whether one works in industry or in academia:

I feel in the long run, causal inference will grow to be an increasing number of central and we are going to see a convergence between the theoretical approach from the social sciences and the data-driven approach from computer science.

We hope you read the remainder of our energetic conversation; within the meantime, Matteo’s statement inspired us to dive into our archives in quest of other insightful articles on causal inference and the subject of causality more broadly. The resulting selection we’re sharing on this Monthly Edition goes from the introductory to the more advanced, and showcases a number of the different approaches data science and ML practitioners use on daily basis of their work.

We hope you enjoy exploring these beneficial reads! As at all times, we’re grateful that you simply’ve made TDS a part of your learning journey; in case you’d prefer to support our work in other ways (and gain access to our entire archive along the best way), please consider becoming Medium members.

TDS Editors

We were thrilled to welcome a complete recent cohort of TDS authors in February — they include Samantha Hodder, Alvaro Peña, Temitope Sobodu, Frederik Holtel, Gil Shomron, Rafael Bischof, Sean Smith, Bruno Alvisio, Joris Guerin, Dmitrii Eliuseev, Kory Becker, Pol Marin, Piotr Lachert, Bruno Ponne, and Noble Ackerson, amongst others. If you will have an interesting project or idea to share with us, we’d love to listen to from you!

See you next month.