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!
Join us for a workshop exploring the exciting field of causal inference and its applications in natural language processing (NLP).
The workshop is addressed to people who want to enrich their NLP and/or Causal Inference toolkits and enhance their perspective on contemporary machine learning.
The workshop will start with an overview of modern causality frameworks. We’ll discuss the most prominent ideas in Causal NLP and present an overview of Causal NLP tasks. Finally, we’ll implement CausalBERT model and demonstrate how it can be used to estimate causal effects in practical contexts.
The workshop is open to everyone, yet to fully enjoy the content, it’s recommend that you:
• Have a solid understanding of Python fundamentals (lists, dicts, scientific stack)
• Understand the basics of graph theory (nodes, directed and undirected edges)
• Have a good understanding of deep learning basics
• Have a good understanding of NLP concepts like tokens and embeddings
The goal of this talk is to give you practical understanding of how to implement Causal NLP methods and inspire you to explore the fast growing world of causality.
