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Causal Discovery - An Introduction | PyData Hamburg Meetup March 2022
Discover the basics of causal discovery in machine learning, explore methods and applications, and learn how to identify causal relationships to improve model performance and decision-making.
- Causal discovery is the process of identifying causal relationships between variables in data.
- Machine learning is not naturally equipped to handle causality and often assumes independence between variables.
- Causal discovery can improve model performance and help account for confounding variables.
- There are three main families of causal discovery methods: PC algorithm, GES, and score-based methods.
- The PC algorithm is a popular method for causal discovery, but it has limitations and may not perform well in all situations.
- Causal graphs can be used to represent causal relationships, and there are different types of graphs that can be used to represent different types of relationships.
- Markov equivalence class is a concept in causal graph theory that refers to a set of graphs that are equivalent in terms of causal relationships.
- Causal discovery can be applied to a wide range of domains, including finance, medicine, and social sciences.
- Regularization techniques can be used to improve the performance of machine learning models in the context of causal discovery.
- Causal inference is the process of estimating causal effects from observational data, and it is a crucial step in making decisions in many fields.
- There are different definitions of causality, and there is no single “correct” definition.
- Causal discovery can be used to identify causal relationships and make decisions in complex systems.
- Confounding variables can be problematic in machine learning and can affect the performance of models.
- Causal discovery can help to account for confounding variables and improve the performance of machine learning models.