Causal Inference and Temporal Distribution Generalization

The field of causal inference is moving towards more efficient and scalable methods for learning Bayesian network structures and discovering causal relationships. Researchers are exploring novel approaches that leverage machine learning techniques, such as the Tsetlin Machine, to constrain the search space and improve computational efficiency. Additionally, there is a growing interest in temporal distribution generalization, particularly in the context of recommender systems, where probabilistic frameworks are being developed to address the challenges of temporal distribution shift. Noteworthy papers in this area include:

  • A study that proposes a novel approach using the Tsetlin Machine to learn Bayesian network structures more efficiently, reducing computational complexity while maintaining competitive accuracy.
  • A paper that presents a probabilistic framework, ELBO_TDS, for temporal distribution generalization in industry-scale recommender systems, achieving superior temporal generalization and a significant uplift in GMV per user.

Sources

Scalable Bayesian Network Structure Learning Using Tsetlin Machine to Constrain the Search Space

Learning Subgroups with Maximum Treatment Effects without Causal Heuristics

Causal Feature Selection for Weather-Driven Residential Load Forecasting

A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems

Context-Specific Causal Graph Discovery with Unobserved Contexts: Non-Stationarity, Regimes and Spatio-Temporal Patterns

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