Missing not at random (MNAR) data pose significant challenges for causal inference, particularly when both confounders and the outcome are partially observed. Without additional assumptions beyond those required for causal inference, causal estimands are generally not identifiable under MNAR mechanisms. This paper first develops identification results for the causal estimand, the average treatment effect, under several plausible MNAR mechanisms using completeness conditions, and proposes an estimation approach based on the Expectation-Maximization (EM) algorithm. We further extend this identification and estimation framework to mediation analysis, enabling the estimation of natural direct and indirect effects under MNAR mechanisms. Through extensive simulation studies, we compare the proposed method with two widely used approaches for handling missing data, complete-case analysis and multiple imputation. The results show that the proposed method yields substantially lower bias under the considered MNAR mechanisms. Finally, we apply the proposed approach to NHANES data to estimate the causal effect of education on depression, with health condition as the mediator.
We study the impact of conditional complier average causal effect (CCACE) estimation methods on the performance of subgroup discovery and heterogeneous causal effect estimation under imperfect compliance. Building on the Bayesian Causal Forest with Instrumental Variable (BCF-IV) (Bargagli-Stoffi et al. (2022)) method,...
Mediation analysis is a powerful tool for elucidating the causal mechanisms by which exposures influence outcomes through mediators. However, conventional approaches often yield biased estimates of mediation effects in scenarios involving high‐dimensional exposures and high‐dimensional mediators, alongside a small numb...
Zhao-Yang Li, Lu-Lu Pan, Yong-Fu Yu et al.· Statistics in Medicine· 0 citations
Longitudinal observational data are often subject to informative missingness. For example, in healthcare research using electronic health records, covariates and outcomes are measured only when patients interact with the healthcare system. Whether these data are observed usually depends on the unobserved variable value...
This work develops a model-free and constraint-query optimal statistical inference framework for causal discovery under latent variables and selection using single-target interventions, and introduces the system-induced subgraph (SIS) to capture the causal relations among system variables while accounting for context v...
Xiao-Tian Hou, Kwangmoon Park, Hong-Zhe Li· 0 citations
Mediation analyses are increasingly conducted in randomized trials, but a sample size adequate for the total treatment effect may leave the natural indirect effect (NIE) or natural direct effect (NDE) substantially underpowered. Randomization does not extend to the mediator, so precision depends on the conditional medi...
It is shown that summary-level homogeneity is implied by the following conditions: the causal effect is additive linear, but not necessarily constant across, all strata of the population; and uncorrelatedness between heterogeneity in the causal effect and in the association between each genetic variant and the exposure...
F. Hartwig, N. Davies, George Davey Smith· 2 citations
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