With over a dozen new papers accepted at NeurIPS 2023, causal inference has exploded in popularity, attracting a large amount of talent and interest from top researchers and institutions including industry giants like Amazon and Microsoft. Text data, with its high complexity, posits an exciting challenge for the causal inference community.
In the presentation, we’ll review the latest advances in 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!
Key Takeaways:
1. Find out more about the more recent breakthroughs in the world of Causal NLP
2. Learn how to de-confound text using BERT, a neural network-based technique for language processing.
3. Understand the benefits, the applications, and the fundamentals of Causal AI