Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference | Paper Citation Record

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This paper cites Causal Inference: What If.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Causal Inference: What If

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Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Unresolved cited work

Reference 2

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This paper cites Recoverability and estimation of causal effects under typical multivariable missingness mechanisms.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Recoverability and estimation of causal effects under typical multivariable missingness mechanisms

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This paper cites Multiple imputation for nonresponse in surveys, volume 81.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Multiple imputation for nonresponse in surveys, volume 81

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This paper cites Multiple imputation of discrete and continuous data by fully con- ditional specification.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Multiple imputation of discrete and continuous data by fully con- ditional specification

Reference 5

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This paper cites Multiple imputation and its application.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Multiple imputation and its application

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This paper cites Multiple-imputation inferences with uncongenial sources of input.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Multiple-imputation inferences with uncongenial sources of input

Reference 7

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This paper cites On the stationary distribution of iterative imputations.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference On the stationary distribution of iterative imputations

Reference 8

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Observation 848ec8a2-a1cb-4351-b93c-8dfb15abfd65 · outbound

This paper cites Multiple imputation of covariates by fully conditional specification: accommodating the substantive model.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Multiple imputation of covariates by fully conditional specification: accommodating the substantive model

Reference 9

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This paper cites A stacked approach for chained equations multiple imputation incorporating the substantive model.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference A stacked approach for chained equations multiple imputation incorporating the substantive model

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This paper cites miss- ing at random.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference miss- ing at random

Reference 11

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This paper cites Canonical causal diagrams to guide the treatment of missing data in epidemiologic studies.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Canonical causal diagrams to guide the treatment of missing data in epidemiologic studies

Reference 12

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This paper cites Assumptions and analysis planning in studies with missing data in multiple vari- ables: moving beyond the mcar/mar/mnar classification.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Assumptions and analysis planning in studies with missing data in multiple vari- ables: moving beyond the mcar/mar/mnar classification

Reference 13

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This paper cites Graphical models for inference with missing data.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Graphical models for inference with missing data

Reference 14

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This paper cites On the use of the not-at-random fully conditional specification (narfcs) procedure in practice.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference On the use of the not-at-random fully conditional specification (narfcs) procedure in practice

Reference 15

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This paper cites Multiple imputation under missing not at random assumptions via fully conditional specification.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Multiple imputation under missing not at random assumptions via fully conditional specification

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This paper cites A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data

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

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Implementation of g- computation on a simulated data set: demonstration of a causal inference technique

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This paper cites Cannabis use and mental health in young people: cohort study.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Cannabis use and mental health in young people: cohort study

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This paper cites The manual of cis-r.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference The manual of cis-r

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This paper cites A new approach to causal inference in mortality studies with a sus- tained exposure period—application to control of the healthy worker survivor effect.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference A new approach to causal inference in mortality studies with a sus- tained exposure period—application to control of the healthy worker survivor effect

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This paper cites Graphical models for recovering probabilistic and causal queries from missing data.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Graphical models for recovering probabilistic and causal queries from missing data

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This paper cites Formalizing subjective notions about the effect of nonrespondents in sample surveys.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Formalizing subjective notions about the effect of nonrespondents in sample surveys

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This paper cites Recent developments in the prevention and treatment of missing data.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Recent developments in the prevention and treatment of missing data

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This paper cites Appropriate inclusion of interactions was needed to avoid bias in multiple imputation.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Appropriate inclusion of interactions was needed to avoid bias in multiple imputation

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This paper cites How should variable selection be performed with multiply imputed data? Statistics in medicine , 27(17):3227– 3246, 2008.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference How should variable selection be performed with multiply imputed data? Statistics in medicine , 27(17):3227– 3246, 2008

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This paper cites Accounting for not-at-random missingness through imputation stacking.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Accounting for not-at-random missingness through imputation stacking

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This paper cites Bootstrap inference for multiple im- putation under uncongeniality and misspecification.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Bootstrap inference for multiple im- putation under uncongeniality and misspecification

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This paper cites R: A Language and Environment for Statistical Computing.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference R: A Language and Environment for Statistical Computing

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Observation 6c7e5765-b83b-43a0-ada2-e281e3cb19b5 · outbound

This paper cites The impact of non-response bias due to sampling in public health studies: A comparison of voluntary versus mandatory recruitment in a dutch national survey on adolescent health.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference The impact of non-response bias due to sampling in public health studies: A comparison of voluntary versus mandatory recruitment in a dutch national survey on adolescent health

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This paper cites Multiple imputation using chained equations: issues and guidance for practice.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Multiple imputation using chained equations: issues and guidance for practice

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This paper cites Identification in missing data models represented by directed acyclic graphs.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Identification in missing data models represented by directed acyclic graphs

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This paper cites Full law identification in graph- ical models of missing data: Completeness results.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Full law identification in graph- ical models of missing data: Completeness results

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This paper cites Adjustment criteria for recovering causal effects from missing data.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Adjustment criteria for recovering causal effects from missing data

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Observation bcfd7854-ba9b-4b15-b3e5-217eecef87c6 · outbound

This paper cites Causal inference with confounders missing not at random.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Causal inference with confounders missing not at random

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This paper cites On varieties of doubly robust estimators under missingness not at random with a shadow variable.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference On varieties of doubly robust estimators under missingness not at random with a shadow variable

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This paper cites An instrumental variable approach for identification and estimation with nonignorable nonresponse.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference An instrumental variable approach for identification and estimation with nonignorable nonresponse

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This paper cites A new instrumental method for dealing with endogenous selection.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference A new instrumental method for dealing with endogenous selection

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Observation 3c74c9f3-8850-49c3-a4bf-af0082efe299 · outbound

This paper cites Identifiability of subgroup causal effects in randomized experiments with nonignorable missing covariates.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Identifiability of subgroup causal effects in randomized experiments with nonignorable missing covariates

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:55:51.534698Z

Source-reported events for the cited work

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Observation 1bb67e9a-419e-4550-9958-f593338c9bf5 · outbound

This paper cites Semiparametric inference of causal effect with nonig- norable missing confounders.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Semiparametric inference of causal effect with nonig- norable missing confounders

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Observation ee7d6675-5e8a-4e71-a9f1-4631230e2bdd · outbound

This paper cites R vignettes: smcfcs, 2022.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference R vignettes: smcfcs, 2022

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Source-reported events for the cited work

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Observation 48fd90a2-24cd-4799-b159-708d28650b92 · outbound

This paper cites A cautious note on auxiliary variables that can increase bias in missing data problems.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference A cautious note on auxiliary variables that can increase bias in missing data problems

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0a32cd33-1d26-4bf9-bb37-6f45032f3bfd · outbound

This paper cites Multiple imputation of missing data under missing at random: including a collider as an auxiliary variable in the imputation model can induce bias.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Multiple imputation of missing data under missing at random: including a collider as an auxiliary variable in the imputation model can induce bias

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:55:51.483127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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This paper cites The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:55:51.471312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b14cb6ba-e13e-4bb1-bf20-1e7b5b9b1108 · outbound

This paper cites Importance sampling.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Importance sampling

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:55:51.458437Z

Source-reported events for the cited work

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Observation e1ba130a-8b98-4c66-9de1-51b2523ea8f4 · outbound

This paper cites Using simulation studies to evaluate statistical methods.

Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference Using simulation studies to evaluate statistical methods

Reference 46

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