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Found 3 papers, 2 papers with code
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic.
On the number of genealogical ancestors tracing to the source groups of an admixed population
Using a mechanistic model of admixture, we characterize admixture genealogically: how many distinct ancestors from the source populations does the admixture represent?
Large-Scale Differentiable Causal Discovery of Factor Graphs
Combining this novel structural assumption with recent advances that bridge the gap between causal discovery and continuous optimization, we achieve causal discovery on thousands of variables.
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