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Learning to mitigate externalities: the coase theorem with hindsight rationality
Scheid, Antoine Capitaine, Aymeric Boursier, Etienne Moulines, Eric Jordan, Michael I. Durmus, Alain
Scheid, Antoine
Capitaine, Aymeric
Boursier, Etienne
Moulines, Eric
Jordan, Michael I.
Durmus, Alain
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2024
License
Language
English
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Abstract
Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures. To achieve this, we explore a tensor rank condition on contingency tables for an observed variable set Xp, showing that the rank is determined by the minimum support of a specific conditional set (not necessary in Xp) that d-separates all variables in Xp. By this, one can locate the latent variable through probing the rank on different observed variables set, and further identify the latent causal structure under some structure assumptions. We present the corresponding identification algorithm and conduct simulated experiments to verify the effectiveness of our method. In general, our results elegantly extend the identification boundary for causal discovery with discrete latent variables and expand the application scope of causal discovery with latent variables.
Citation
G.-Y. Hao, J. Zhang, B. Huang, H. Wang, and K. Zhang, “Natural Counterfactuals With Necessary Backtracking,” Adv Neural Inf Process Syst, vol. 37, pp. 14962–14995, Dec. 2024.
Source
Advances in Neural Information Processing Systems (NeurIPS 2024)
Conference
Keywords
Externalities, Coase theorem, Social welfare, Bargaining strategies, Multi-armed bits
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Source
Publisher
NEURIPS