AI BriefingTrends in Causal Representation Learning Research - LG AI Research BLOG
Key point
This piece covers the necessity and principles of Causal Representation Learning, which reasons about causality beyond correlation to overcome the limitations of existing machine learning.
Details
Current machine learning algorithms presuppose that data has the same distribution under the i.i.d (independent and identically distributed) assumption. However, in reality, the domain shift problem occurs where data distributions change, and this becomes a major cause undermining the Robustness of models.
Existing models only capture correlation (Association) between data, but fail to infer causality (Causality). To address this, Causal Representation Learning aims to modularize and represent physical mechanisms, thereby learning knowledge that can be reused in new environments and improving the generalization performance of models.
The stages of causal modeling are classified as follows according to the Ladder of Causation proposed by Judea Pearl.
- Associational Causality: Predicting the probability of outcomes given inputs (in an i.i.d environment)
- Interventional Causality: Inferring the effect of changing a specific variable
- Counterfactual Causality: Inferring hypothetical 'what if' scenarios about events that have already occurred
This research presents a direction for learning causal representations from data and overcoming the limitations of existing machine learning by leveraging the principles of Independent Causal Mechanism(ICM) and Sparse Mechanism Shift(SMS).
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