A Two-Stage Joint Modeling Method for Causal Mediation Analysis in the Presence of Treatment Noncompliance

Park, Soo‐Jin; Kürüm, Esra

doi:10.1515/jci-2019-0019

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…However, if the identification only relies on normality, estimates are sensitive to the inclusion/exclusion of predictors used to model compliance (Copas & Li, 1997; Zhang et al, 2009) or other structural assumptions (Stuart & Jo, 2015). The same pattern was shown for the CACME estimator (Park & Kurum, 2020).…”

Section: Estimation Methods Consideredsupporting

confidence: 75%

“…The CACME estimate is obtained by applying mediator and outcome models to Equation 1. As a result, the CACME is estimated as δ c ( z ) = true α ^ c z × ( true β ^ c m + true β ^ c m z z ) (Park & Kurum, 2020). We used Mplus Version 8 (Muthén & Muthén, 1998–2017) to carry out this analysis.…”

Section: Estimation Methods Consideredmentioning

confidence: 99%

“…The IV-based method proposed by Yamamoto (2014) does not require any distributional assumption and has the advantage of flexibility to accommodate different distributions. In contrast, the ML method proposed by Park and Kurum (2020) requires an additional parametric distributional assumption with respect to the compliance, mediator, and outcome. The ML method is a result of extending the complier average causal effects (CACE) estimation method (see, e.g., Imbens & Rubin, 1997; Little & Yau, 1998; Yau & Little, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity Evaluation of Methods for Estimating Complier Average Causal Mediation Effects to Assumptions

Park¹,

Palardy

2020

Journal of Educational and Behavioral Statistics

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Estimating the effects of randomized experiments and, by extension, their mediating mechanisms, is often complicated by treatment noncompliance. Two estimation methods for causal mediation in the presence of noncompliance have recently been proposed, the instrumental variable method (IV-mediate) and maximum likelihood method (ML-mediate). However, little research has examined their performance when certain assumptions are violated and under varying data conditions. This article addresses that gap in the research and compares the performance of the two methods. The results show that the distributional assumption of the compliance behavior plays an important role in estimation. That is, regardless of the estimation method or whether the other assumptions hold, results are biased if the distributional assumption is not met. We also found that the IV-mediate method is more sensitive to exclusion restriction violations, while the ML-mediate method is more sensitive to monotonicity violations. Moreover, estimates depend in part on compliance rate, sample size, and the availability and impact of control covariates. These findings are used to provide guidance on estimator selection.

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Section: Estimation Methods Consideredsupporting

confidence: 75%

Section: Estimation Methods Consideredmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensitivity Evaluation of Methods for Estimating Complier Average Causal Mediation Effects to Assumptions

Park¹,

Palardy

2020

Journal of Educational and Behavioral Statistics

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“…To address non-compliance, one can assume that the response in the dataset follows a specific distribution, such as exponential families, and use maximum likelihood estimation. The authors in [6][7][8] presented the estimation of the complier average causal effect (CACE) with the maximum likelihood estimation method with the EM algorithm [9]. The maximum likelihood method provides the advantage of relaxing the exclusion restrictions, which can often be unrealistic, particularly in natural experiment scenarios [10].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Complier Average Causal Effect with Non-Ignorable Missing Outcomes Using Likelihood Analysis

Du,

Wen,

Liang

2024

Mathematics

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Missing data problems arise in randomized trials, which complicates the inference of causal effects if the missing mechanism is non-ignorable. We tackle the challenge of identifying and estimating the complier average causal effect parameters under non-ignorable missingness by increasing the covariates to mitigate the sensitivity to the violation of specific identification assumptions. The missing data mechanism is assumed to follow a logistic model, wherein the absence of the outcome is explained by the outcome itself, the treatment received, and the covariates. We establish the identifiability of the models under mild conditions by assuming that the outcome follows a normal distribution. We develop a computational method to estimate model parameters through a two-step likelihood estimation approach, employing subgroup analysis. The bootstrap method is employed for variance estimation, and the effectiveness of our approach is confirmed through simulation. We applied the proposed method to analyze the household income dataset from the Chinese Household Income Project Survey 2013.

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An introduction to causal mediation analysis

Qin

2024

Asia Pacific Educ. Rev.

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Causal mediation analysis has gained increasing attention in recent years. This article guides empirical researchers through the concepts and challenges of causal mediation analysis. I first clarify the difference between traditional and causal mediation analysis and highlight the importance of adjusting for the treatment-by-mediator interaction and confounders of the treatment–mediator, treatment–outcome, and mediator–outcome relationships. I then introduce the definition of causal mediation effects under the potential outcomes framework and different methods for the identification and estimation of the effects. After that, I highlight the importance of conducting a sensitivity analysis to assess the sensitivity of analysis results to potential unmeasured confounding. I also list various statistical software that can conduct causal mediation analysis and sensitivity analysis and provide suggestions for writing a causal mediation analysis paper. Finally, I briefly introduce some extensions that I made with my colleagues, including power analysis, multisite causal mediation analysis, causal moderated mediation analysis, and relaxing the assumption of no post-treatment confounding.

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A Two-Stage Joint Modeling Method for Causal Mediation Analysis in the Presence of Treatment Noncompliance

Cited by 4 publications

References 26 publications

Sensitivity Evaluation of Methods for Estimating Complier Average Causal Mediation Effects to Assumptions

Sensitivity Evaluation of Methods for Estimating Complier Average Causal Mediation Effects to Assumptions

Estimating the Complier Average Causal Effect with Non-Ignorable Missing Outcomes Using Likelihood Analysis

An introduction to causal mediation analysis

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