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Causal Bandits Podcast
Causal AI, Effect Heterogeneity & Understanding ML || Alicia Curth || Causal Bandits Ep. 006 (2023)
Dec 27, 2023
Alicia Curth, a machine learning researcher specializing in causal machine learning, discusses topics such as the double descent phenomenon, conditional average treatment effect estimators, challenges in working with Kate models, curiosity-driven studies, sensitivity analysis in causal research, and contrasting approaches in machine learning and statistics/econometrics.
54:39
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Quick takeaways
- Understanding the underlying structure and assumptions in machine learning models is crucial for comprehensive problem analysis.
- Uncovering the root causes and mechanisms behind complex phenomena in causality leads to a deeper understanding of the problem.
Deep dives
The importance of understanding the problem in causal analysis
In this podcast episode, the speaker emphasizes the significance of stepping back and critically analyzing the problem at hand. Often, researchers focus on developing new methods without fully understanding the existing ones and the limitations they possess. By asking the fundamental question of 'what are we actually doing?', researchers can gain a more comprehensive understanding of the problem, identify gaps in knowledge, and explore different perspectives and approaches.
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