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Paper Citation Record · LEDGER

Incorporating structural uncertainty in causal decision making

As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.23495.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.23495 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:47:03.457147Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation 83be0de5-d8c9-4ece-96e5-b8bb178dd629 · outbound

This paper cites Causal inference in statistics, social, and biomedical sciences.

Incorporating structural uncertainty in causal decision making Causal inference in statistics, social, and biomedical sciences

Reference 1

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Observation e139f2ff-3d23-4b83-86b6-7fc9f7810033 · outbound

This paper cites Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects.

Incorporating structural uncertainty in causal decision making Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects

Reference 2

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Incorporating structural uncertainty in causal decision making Causation, pre- diction, and search

Reference 3

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This paper cites Review of causal discov- ery methods based on graphical models.

Incorporating structural uncertainty in causal decision making Review of causal discov- ery methods based on graphical models

Reference 4

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This paper cites Statistical Decision Theory and Bayesian Analysis.

Incorporating structural uncertainty in causal decision making Statistical Decision Theory and Bayesian Analysis

Reference 5

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This paper cites The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation.

Incorporating structural uncertainty in causal decision making The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation

Reference 6

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This paper cites SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning.

Incorporating structural uncertainty in causal decision making SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning

Reference 7

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This paper cites On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias.

Incorporating structural uncertainty in causal decision making On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias

Reference 8

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This paper cites Optimal structure identification with greedy search.

Incorporating structural uncertainty in causal decision making Optimal structure identification with greedy search

Reference 9

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This paper cites Learning bayesian networks.

Incorporating structural uncertainty in causal decision making Learning bayesian networks

Reference 10

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This paper cites Nonlinear causal discovery with additive noise models.

Incorporating structural uncertainty in causal decision making Nonlinear causal discovery with additive noise models

Reference 11

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This paper cites Causal infer- ence in statistics: A primer.

Incorporating structural uncertainty in causal decision making Causal infer- ence in statistics: A primer

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This paper cites Information-geometric approach to inferring causal directions.

Incorporating structural uncertainty in causal decision making Information-geometric approach to inferring causal directions

Reference 13

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Incorporating structural uncertainty in causal decision making Bayesian graphical models for discrete data

Reference 14

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Incorporating structural uncertainty in causal decision making Unresolved cited work

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Observation fd7e2778-92d1-45ac-b0b0-b6eef8903897 · outbound

This paper cites Bayesian model averaging: A tutorial.

Incorporating structural uncertainty in causal decision making Bayesian model averaging: A tutorial

Reference 16

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Incorporating structural uncertainty in causal decision making Bayesian Model Selection in Social Research , vol- ume 25

Reference 17

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Incorporating structural uncertainty in causal decision making Optimal predictive model selection

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Incorporating structural uncertainty in causal decision making Bayesian adap- tive sampling for variable selection and model averaging

Reference 19

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Incorporating structural uncertainty in causal decision making Rosenbaum

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Incorporating structural uncertainty in causal decision making VanderWeele and Peng Ding

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Incorporating structural uncertainty in causal decision making Tchetgen Tchetgen

Reference 22

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Incorporating structural uncertainty in causal decision making Sensitivity analysis under unmeasured confound- ing: bounds on the average causal effect

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Incorporating structural uncertainty in causal decision making Unresolved cited work

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Incorporating structural uncertainty in causal decision making Unresolved cited work

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Incorporating structural uncertainty in causal decision making Double/debiased machine learning for treat- ment and structural parameters

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Incorporating structural uncertainty in causal decision making Unresolved cited work

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Incorporating structural uncertainty in causal decision making Causal effect estimation under multi- ple candidate causal graphs

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Incorporating structural uncertainty in causal decision making Learning causal structure under model uncertainty

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Incorporating structural uncertainty in causal decision making Dehejia and Sadek Wahba

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Incorporating structural uncertainty in causal decision making Calibrating sensitivity analyses for unmeasured con- founding in observational studies

Reference 32

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Incorporating structural uncertainty in causal decision making Doubly robust estimation of heterogeneous causal effects

Reference 33

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Incorporating structural uncertainty in causal decision making Causality: Models, Reasoning, and Inference

Reference 34

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Incorporating structural uncertainty in causal decision making Causal discovery using regression-based conditional independence tests

Reference 35

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Incorporating structural uncertainty in causal decision making An introduction to the boot- strap

Reference 36

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