Andrew Lawrence, Director of Research at causaLens, shares his insights on the fascinating world of causality and modularity in AI. He discusses his journey from academia to industry, the role of Bayesian non-parametrics in understanding causal relationships, and the importance of collaboration in causal discovery. Andrew highlights the challenges of applying generative AI in high-stakes scenarios and underscores how teamwork is vital in translating research into practical applications. He also reflects on his supportive childhood and the significance of mentorship in tech.
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Andrew's Causality Journey
Andrew Lawrence's causality journey began after his PhD, where he studied Bayesian non-parametrics.
He transitioned from industry to academia and back to industry, joining causaLens in 2019.
insights INSIGHT
Bayesian Non-parametrics and Causality
Bayesian non-parametrics, specifically focusing on conditional probabilities and latent variable models, is applicable to causal inference.
This background helps understand how to factor the joint distribution and capture the true data-generating process.
insights INSIGHT
Deep Learning's Limitations
Deep learning's focus on minimizing in-sample error can lead to overfitting and poor generalization.
Identifying the true drivers of the target variable is crucial for accurate predictions on unseen data.
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In 'The Book of Why', Judea Pearl and Dana Mackenzie delve into the causal revolution, which has transformed the way we distinguish between correlation and causation. The book introduces causal diagrams, such as Directed Acyclic Graphs (DAGs), and explains how to predict the effects of interventions. It addresses fundamental questions about causality and its implications in fields like medicine, economics, and artificial intelligence. The authors also discuss the potential of causal inference in enabling computers to understand counterfactuals and engage in moral decision-making[2][4][5].
Pattern Recognition and Machine Learning
Christopher M. Bishop
This book offers a detailed introduction to pattern recognition and machine learning, integrating both fields under a common statistical framework. It covers topics such as Bayesian methods, graphical models, kernel-based algorithms, and neural networks, making it suitable for advanced undergraduates, first-year PhD students, researchers, and practitioners. The book includes a wide range of exercises and is supported by additional materials like lecture slides and figures.
Elements of Causal Inference
Elements of Causal Inference
Jonas Peters
Dominik Janzing
Bernhard Scholkopf
This book provides a comprehensive introduction to causal inference, covering various methods and techniques for causal analysis. It delves into the fundamental concepts of causality, including directed acyclic graphs (DAGs) and causal diagrams. The book also explores advanced topics such as causal discovery, causal effects estimation, and causal mediation analysis. It is a valuable resource for researchers and practitioners in various fields who want to learn about causal inference.
`from causality import solution` Recorded on Sep 04, 2023 in London, United Kingdom
A Python package that would allow us to address an arbitrary causal problem with a one-liner does not yet exist.
Fortunately, there are other ways to implement and deploy causal solutions at scale.
In this episode, Andrew shares his journey into causality and gives us a glimpse into the behind-the-scenes of his everyday work at causaLens.
We discuss new ideas that Andrew and his team use to enhance the capabilities of available open-source causal packages, how they strive to build and maintain a highly modularized and open platform.
Finally, we talk about the importance of team work and what Andrew's parents did to make him feel nurtured & supported.
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About The Guest Andrew Lawrence is the Director of Research at causaLens (https://causalens.com/) Connect with Andrew:
About The Host Aleksander (Alex) Molak is an independent ML researcher, educator, entrepreneur and a best-selling author in the area of causality. Connect with Alex:
Causal Bandits Podcast 
