Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:44:27.990110Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2411.14003.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:44:27.990110Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
62 of 62 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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Generative Intervention Models for Causal Perturbation Modeling write newline
Reference 1
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Generative Intervention Models for Causal Perturbation Modeling ABCD - Strategy : Budgeted experimental design for targeted causal structure discovery
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Reference 3
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Reference 4
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Generative Intervention Models for Causal Perturbation Modeling R., and Mitavskiy, B
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Generative Intervention Models for Causal Perturbation Modeling Scgpt: toward building a foundation model for single-cell multi-omics using generative ai
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Generative Intervention Models for Causal Perturbation Modeling Sinkhorn distances: Lightspeed computation of optimal transport
Reference 11
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Generative Intervention Models for Causal Perturbation Modeling Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
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Generative Intervention Models for Causal Perturbation Modeling and Sinha, S
Reference 13
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Generative Intervention Models for Causal Perturbation Modeling On the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables
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Generative Intervention Models for Causal Perturbation Modeling On the evolution of random graphs
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Generative Intervention Models for Causal Perturbation Modeling Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
Reference 16
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Generative Intervention Models for Causal Perturbation Modeling The Hill equation and the origin of quantitative pharmacology
Reference 17
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Generative Intervention Models for Causal Perturbation Modeling and Bengio, Y
Reference 18
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Generative Intervention Models for Causal Perturbation Modeling Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks
Reference 19
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Reference 20
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Generative Intervention Models for Causal Perturbation Modeling Large-scale foundation model on single-cell transcriptomics
Reference 21
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Reference 22
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Generative Intervention Models for Causal Perturbation Modeling Predicting cellular responses to novel drug perturbations at a single-cell resolution
Reference 23
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Reference 24
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Generative Intervention Models for Causal Perturbation Modeling Categorical reparameterization with gumbel-softmax
Reference 25
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Generative Intervention Models for Causal Perturbation Modeling M., Jindal, K., Solnica-Krezel, L., and Morris, S
Reference 26
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Observation 75e74d21-e77a-4c70-b3df-f810af029ca4 · outbound
Generative Intervention Models for Causal Perturbation Modeling Unresolved cited work
Reference 27
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Generative Intervention Models for Causal Perturbation Modeling T., and Dudley, J
Reference 28
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Generative Intervention Models for Causal Perturbation Modeling B., Jordan, M
Reference 29
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Generative Intervention Models for Causal Perturbation Modeling Large-scale differentiable causal discovery of factor graphs
Reference 30
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Generative Intervention Models for Causal Perturbation Modeling DiBS : Differentiable Bayesian structure learning
Reference 31
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Generative Intervention Models for Causal Perturbation Modeling A., and Theis, F
Reference 32
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Reference 33
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Reference 34
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Reference 35
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Reference 37
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Reference 38
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Reference 40
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Reference 42
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Reference 44
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Generative Intervention Models for Causal Perturbation Modeling BACKSHIFT : Learning causal cyclic graphs from unknown shift interventions
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Reference 46
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Reference 47
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Reference 48
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Reference 49
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Reference 50
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Reference 51
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Reference 52
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Reference 53
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Reference 55
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Reference 58
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Reference 59
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Reference 62
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