Jakob Zeitler is a Pioneer Fellow at SMARTbiomed and a PhD graduate from the UCL Centre for Artificial Intelligence. In this conversation, we explore the intersection of causal inference and computer science, focusing on its theoretical foundations and practical applications. We discuss how frameworks like Directed Acyclic Graphs (DAGs) and potential outcomes are used to model causality, the differences between observational and experimental studies, and the growing role of causal inference in AI and machine learning. The conversation includes real-world examples, industry use cases, and guidance for applying causal methods in both academic and business settings.

Expect to learn about

- The fundamentals of causal inference, including DAGs and potential outcomes.
- The practical differences between observational and experimental studies.
- How causal reasoning is applied in fields like public health, forecasting, and logistics.
- The challenges and opportunities of integrating causal inference with machine learning.
- Methods such as proximal learning and synthetic control.
- The limitations of causal discovery tools in business contexts.
- How industries are leveraging experimentation and causal inference for decision-making.

Where to find Jakob

Website: https://jakobzeitler.github.io/
LinkedIn: https://www.linkedin.com/in/jakobzeitler/


Where to find Ida

Website: https://idajohnsson.com/
LinkedIn: https://www.linkedin.com/in/ida-johnsson/
X: https://twitter.com/IdaBJohnsson

Timestamps

00:00 Preview
00:00 Intro
00:01 Causal Inference in Computer Science
00:04 DAGs vs. potential outcomes
00:07 Proximal learning
00:16 Theory vs. practice
00:20 Making assumptions & observational data
00:26 The cost of assumptions
00:33 Including disclaimers
00:36 Causality in forecasting
00:41 What applied problems do Computer Scientists work on?
00:43 Causal discovery
00:51 CS vs. economist training
00:59 Causal inference in everyday life