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Which models work best for causal discovery and double machine learning?

In this extra episode, we present 4 more conversations with the researchers presenting their work at the CLeaR 2024 conference in Los Angeles, California.

What you'll learn:
- Which causal discovery models perform best with their default hyperparameters?
- How to tune your double machine learning model?
- Does putting your paper on ArXiv early increase its chances of being accepted at a conference?
- How to deal with causal representation learning with multiple latent interventions?

Time codes:
00:24 Damian Machlanski - Hyperparameter Tuning for Causal Discovery
08:52 Oliver Schacht - Hyperparameter Tuning for DML
14:41 Yanai Elazar - Causal Effect of Early ArXiving on Paper Acceptance
18:53 Simon Bing - Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions

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✅  About Causal Python with Alex Molak.

Welcome to my official YouTube channel, Causal Python, with Alex Molak. Dive into the fascinating world of Causal AI, unraveling the complexities of Causal Inference and Discovery with Python. 

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