To apply or not to apply, that is the question.
Causal reasoning elevates predictive outcomes by shifting from “what happened” to “what would happen if”. Yet, implementing causality can be challenging or even infeasible in some contexts. This talk explores how the very act of assessing its applicability can add value to your projects. Through a gentle introduction to causal inference tools and practical use cases, you will learn how to bring greater scientific rigour to real-world problems.
No prior knowledge is assumed but for the seasoned practitioners I hope to shine light on aspects that may not have been considered.
Although causal inference techniques have immense potential, their practical application requires careful consideration of context and limitations. The act of engaging in causality is valuable in its own right, promoting a more rigorous and insightful approach to a project. In particular, “causal thinking” through Causal Graph construction and critical assessment of assumptions, is a mental exercise that enhances project rigour which may lead to a deeper understanding of what is possible with the data.
A key focus will be on Directed Acyclic Graphs (DAGs) as powerful tools for causal thinking. They provide visual aids of the data generation process, mapping out the relationships between parameters and their dependencies. We’ll examine how DAGs articulate the understanding of a system. A second focus will be on Identifiability a process of identifying minimum sets of parameters required to answer cause and effect questions.
By the end of this talk, you’ll have a nuanced understanding of CI, and appreciate its potential and limitations. You’ll be equipped to assess when it’s the right tool for the job, and you’ll gain valuable insights into how “causal thinking” can enhance your work especially with real-world data.
Further details are in this Towards Data Science article: https://bit.ly/causal-hygiene
Technical level: High Level/overview
Session Length: 40 minutes
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