With an average of 3.2 new papers published on Arxiv every day in 2022, causal inference has exploded in popularity, attracting large amount of talent and interest from top researchers and institutions including industry giants like Amazon or Microsoft. Text data, with its high complexity, posits an exciting challenge for causal inference community. In the workshop, we'll review the latest advances in the field of Causal NLP and implement a causal Transformer model to demonstrate how to translate these developments into a practical solution that can bring real business value. All in Python!

Aleksander Molak

Affiliation: CausalPython.io / Lespire.io

For the last 6 years Alex worked as a machine learning consultant, engineer and researcher in the industry and academia. He helped designing and building machine learning systems for Fortune 100, Fortune 500 and Inc 5000 companies.

He’s an international speaker, blogger and author of a book on causality in Python. Interested in causality, NLP, probabilistic modeling, representation learning and graph neural networks.

Loves traveling with his wife, passionate about vegan food, languages and running.

Website: https://alxndr.io LinkedIn: https://www.linkedin.com/in/aleksandermolak/

visit the speaker at: GithubHomepage