Constructing g-computation estimators: two case studies in selection bias | Paper Citation Record

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This paper cites On the Use of Covariate Supersets for Identification Conditions,.

Constructing g-computation estimators: two case studies in selection bias On the Use of Covariate Supersets for Identification Conditions,

Reference 1

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Observation d9b6b186-88be-4950-a542-7b0bf161f46a · outbound

This paper cites Toward a Clearer Definition of Selection Bias When Estimating Causal Effects,.

Constructing g-computation estimators: two case studies in selection bias Toward a Clearer Definition of Selection Bias When Estimating Causal Effects,

Reference 2

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This paper cites Selection Bias Requires Selection: The Case of Collider Stratification Bias,.

Constructing g-computation estimators: two case studies in selection bias Selection Bias Requires Selection: The Case of Collider Stratification Bias,

Reference 3

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This paper cites The Evolution of Selection Bias in the Recent Epidemiologic Literature—A Selective Overview,.

Constructing g-computation estimators: two case studies in selection bias The Evolution of Selection Bias in the Recent Epidemiologic Literature—A Selective Overview,

Reference 4

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This paper cites A Potential Outcomes Approach to Selection Bias,.

Constructing g-computation estimators: two case studies in selection bias A Potential Outcomes Approach to Selection Bias,

Reference 5

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This paper cites Simple graphical rules for assessing selection bias in general-population and selected-sample treatment effects,.

Constructing g-computation estimators: two case studies in selection bias Simple graphical rules for assessing selection bias in general-population and selected-sample treatment effects,

Reference 6

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This paper cites Marginal structural models and causal inference in epidemiology,.

Constructing g-computation estimators: two case studies in selection bias Marginal structural models and causal inference in epidemiology,

Reference 7

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This paper cites Estimating marginal structural model parameters for time-fixed, binary actions with g-computation and estimating equations,.

Constructing g-computation estimators: two case studies in selection bias Estimating marginal structural model parameters for time-fixed, binary actions with g-computation and estimating equations,

Reference 8

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This paper cites Reflection on modern methods: combining weights for con- founding and missing data,.

Constructing g-computation estimators: two case studies in selection bias Reflection on modern methods: combining weights for con- founding and missing data,

Reference 9

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This paper cites an unresolved cited work.

Constructing g-computation estimators: two case studies in selection bias Unresolved cited work

Reference 10

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This paper cites Implementation of G-computation on a simulated data set: demonstration of a causal inference technique,.

Constructing g-computation estimators: two case studies in selection bias Implementation of G-computation on a simulated data set: demonstration of a causal inference technique,

Reference 11

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This paper cites G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study,.

Constructing g-computation estimators: two case studies in selection bias G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study,

Reference 12

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This paper cites A Practical Example Demonstrating the Utility of Single-world Intervention Graphs,.

Constructing g-computation estimators: two case studies in selection bias A Practical Example Demonstrating the Utility of Single-world Intervention Graphs,

Reference 13

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This paper cites The Calculus of M-Estimation,.

Constructing g-computation estimators: two case studies in selection bias The Calculus of M-Estimation,

Reference 14

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This paper cites an unresolved cited work.

Constructing g-computation estimators: two case studies in selection bias Unresolved cited work

Reference 15

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This paper cites M-estimation for common epidemiological measures: intro- duction and applied examples,.

Constructing g-computation estimators: two case studies in selection bias M-estimation for common epidemiological measures: intro- duction and applied examples,

Reference 16

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This paper cites Estimating Equations, Theory of,.

Constructing g-computation estimators: two case studies in selection bias Estimating Equations, Theory of,

Reference 17

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This paper cites Estimating functions and the generalized method of moments,.

Constructing g-computation estimators: two case studies in selection bias Estimating functions and the generalized method of moments,

Reference 18

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This paper cites Carroll, David Ruppert, Leonard A.

Constructing g-computation estimators: two case studies in selection bias Carroll, David Ruppert, Leonard A

Reference 19

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This paper cites Bootstrap Methods: Another Look at the Jackknife,.

Constructing g-computation estimators: two case studies in selection bias Bootstrap Methods: Another Look at the Jackknife,

Reference 20

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This paper cites Sampling distributions and the bootstrap,.

Constructing g-computation estimators: two case studies in selection bias Sampling distributions and the bootstrap,

Reference 21

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This paper cites On Variance of the Treatment Effect in the Treated When Estimated by Inverse Probability Weighting,.

Constructing g-computation estimators: two case studies in selection bias On Variance of the Treatment Effect in the Treated When Estimated by Inverse Probability Weighting,

Reference 22

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This paper cites Delicatessen: M-Estimation in Python.

Constructing g-computation estimators: two case studies in selection bias Delicatessen: M-Estimation in Python

Reference 23

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This paper cites The Calculus of M-Estimation in R with geex,.

Constructing g-computation estimators: two case studies in selection bias The Calculus of M-Estimation in R with geex,

Reference 24

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This paper cites Estimating causal effects from epidemiological data,.

Constructing g-computation estimators: two case studies in selection bias Estimating causal effects from epidemiological data,

Reference 25

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This paper cites The consistency statement in causal inference: a definition or an assumption?,.

Constructing g-computation estimators: two case studies in selection bias The consistency statement in causal inference: a definition or an assumption?,

Reference 26

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This paper cites Positivity: Identifiability and Estimability.

Constructing g-computation estimators: two case studies in selection bias Positivity: Identifiability and Estimability

Reference 27

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This paper cites Using simulation studies to evaluate statistical methods,.

Constructing g-computation estimators: two case studies in selection bias Using simulation studies to evaluate statistical methods,

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Constructing g-computation estimators: two case studies in selection bias Array programming with NumPy,

Reference 29

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This paper cites Scipy 1.0: fundamental algorithms for scientific computing in Python,.

Constructing g-computation estimators: two case studies in selection bias Scipy 1.0: fundamental algorithms for scientific computing in Python,

Reference 30

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This paper cites Data Structures for Statistical Computing in Python,.

Constructing g-computation estimators: two case studies in selection bias Data Structures for Statistical Computing in Python,

Reference 31

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This paper cites Empirical Sandwich Variance Estimator for Iterated Conditional Expectation g-Computation,.

Constructing g-computation estimators: two case studies in selection bias Empirical Sandwich Variance Estimator for Iterated Conditional Expectation g-Computation,

Reference 32

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This paper cites Leveraging external validation data: the challenges of transporting measurement error parameters,.

Constructing g-computation estimators: two case studies in selection bias Leveraging external validation data: the challenges of transporting measurement error parameters,

Reference 33

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This paper cites Targeted Learning of the Mean Outcome under an Optimal Dynamic Treatment Rule,.

Constructing g-computation estimators: two case studies in selection bias Targeted Learning of the Mean Outcome under an Optimal Dynamic Treatment Rule,

Reference 34

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This paper cites Synthesis estimators for transportability with positivity violations by a continuous covariate,.

Constructing g-computation estimators: two case studies in selection bias Synthesis estimators for transportability with positivity violations by a continuous covariate,

Reference 35

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This paper cites Econometric methods for fractional response variables with an application to 401(k) plan participation rates,.

Constructing g-computation estimators: two case studies in selection bias Econometric methods for fractional response variables with an application to 401(k) plan participation rates,

Reference 36

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This paper cites Quasi-Likelihood and Optimal Estimation,.

Constructing g-computation estimators: two case studies in selection bias Quasi-Likelihood and Optimal Estimation,

Reference 37

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This paper cites Revisiting representativeness,.

Constructing g-computation estimators: two case studies in selection bias Revisiting representativeness,

Reference 38

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Constructing g-computation estimators: two case studies in selection bias Unresolved cited work

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This paper cites Z-estimation and stratified samples: application to survival models,.

Constructing g-computation estimators: two case studies in selection bias Z-estimation and stratified samples: application to survival models,

Reference 40

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This paper cites Estimating equations for causal survival analysis with pooled logistic regression.

Constructing g-computation estimators: two case studies in selection bias Estimating equations for causal survival analysis with pooled logistic regression

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This paper cites The Robust Inference for the Cox Proportional Hazards Model,.

Constructing g-computation estimators: two case studies in selection bias The Robust Inference for the Cox Proportional Hazards Model,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:27.728219Z

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This paper cites Penalized Regressions: The Bridge versus the Lasso,.

Constructing g-computation estimators: two case studies in selection bias Penalized Regressions: The Bridge versus the Lasso,

Reference 43

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This paper cites Penalized Estimating Equations,.

Constructing g-computation estimators: two case studies in selection bias Penalized Estimating Equations,

Reference 44

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This paper cites A unified class of penalties with the capability of producing a differentiable alternative to l1 norm penalty,.

Constructing g-computation estimators: two case studies in selection bias A unified class of penalties with the capability of producing a differentiable alternative to l1 norm penalty,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:27.462847Z

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This paper cites What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems.

Constructing g-computation estimators: two case studies in selection bias What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems

Reference 46

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This paper cites Nonparametric identification is not enough, but randomized controlled trials are.

Constructing g-computation estimators: two case studies in selection bias Nonparametric identification is not enough, but randomized controlled trials are

Reference 47

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This paper cites Targeted maximum likelihood estimation for causal inference in observational studies,.

Constructing g-computation estimators: two case studies in selection bias Targeted maximum likelihood estimation for causal inference in observational studies,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:27.382093Z

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This paper cites Doubly robust estimation of causal effects,.

Constructing g-computation estimators: two case studies in selection bias Doubly robust estimation of causal effects,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:27.233199Z

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This paper cites Demystifying Statistical Learning Based on Efficient Influence Functions,.

Constructing g-computation estimators: two case studies in selection bias Demystifying Statistical Learning Based on Efficient Influence Functions,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:27.137149Z

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This paper cites Pulling back the curtain: the road from statistical estimand to machine-learning based estimator for epidemiologists (no wizard required).

Constructing g-computation estimators: two case studies in selection bias Pulling back the curtain: the road from statistical estimand to machine-learning based estimator for epidemiologists (no wizard required)

Reference 51

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This paper cites Five Facts About Influence Functions,.

Constructing g-computation estimators: two case studies in selection bias Five Facts About Influence Functions,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:27.019385Z

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Observation 7fdd46b2-2ca8-4b0c-801b-014b48db9542 · outbound

This paper cites Double robust variance estimation with parametric working models,.

Constructing g-computation estimators: two case studies in selection bias Double robust variance estimation with parametric working models,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:26.880342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3585d1bf-44b6-4c61-b516-324e11769eda · outbound

This paper cites Double/debiased machine learning for treatment and structural parameters,.

Constructing g-computation estimators: two case studies in selection bias Double/debiased machine learning for treatment and structural parameters,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:26.769020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 03125a1f-da4f-496c-93e1-6eaaab3fd585 · outbound

This paper cites Machine Learning for Causal Inference: On the Use of Cross-fit Estimators,.

Constructing g-computation estimators: two case studies in selection bias Machine Learning for Causal Inference: On the Use of Cross-fit Estimators,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:26.712429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aabc86a7-f8cb-4943-b671-43e347efe68d · outbound

This paper cites Machine Learning and Causal Inference,.

Constructing g-computation estimators: two case studies in selection bias Machine Learning and Causal Inference,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:26.586049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e29ba5a3-9f67-468f-b461-3dec5bed6a8c · outbound

This paper cites The use of plasmodes as a supplement to simulations: a simple example evaluating individual admixture estimation methodologies,.

Constructing g-computation estimators: two case studies in selection bias The use of plasmodes as a supplement to simulations: a simple example evaluating individual admixture estimation methodologies,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:26.439273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c2cc36e1-c01b-4076-85a4-e3180a348e89 · outbound

This paper cites an unresolved cited work.

Constructing g-computation estimators: two case studies in selection bias Unresolved cited work

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-07T11:11:20.708199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers