Research map / Probabilistic and scientific modeling / Causal discovery

Optimal Transport for Structure Learning Under Missing Data

ICML 2024 · Probabilistic and scientific modeling › Causal discovery · approach: Constraint-based structure learning with latents · 1 citations (OpenAlex)

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Proposes an optimal-transport-based approach to jointly perform structure learning and imputation under missing data, addressing the chicken-and-egg problem of causal discovery with missing values.

Problem

Causal structure discovery when data has missing values: imputation needs dependency/causal structure, but structure learning needs complete data, so imputing first and then running structure learning is sub-optimal.

Approach

Uses optimal transport to couple imputation with structure learning so that the imputed distributions and the recovered causal structure are learned together rather than sequentially. The provided text is an ICML formatting template, so the specific method is not described.

Limitations

• The supplied full text is the ICML 2024 formatting template rather than the paper body, so method details and results are unavailable.

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