Corpus ID: 236318513

Post-Treatment Confounding in Causal Mediation Studies: A Cutting-Edge Problem and A Novel Solution via Sensitivity Analysis

@inproceedings{Hong2021PostTreatmentCI,
  title={Post-Treatment Confounding in Causal Mediation Studies: A Cutting-Edge Problem and A Novel Solution via Sensitivity Analysis},
  author={Guanglei Hong and Fan Yang and Xu Qin},
  year={2021}
}
In causal mediation studies that decompose an average treatment effect into a natural indirect effect (NIE) and a natural direct effect (NDE), examples of post-treatment confounding are abundant. Past research has generally considered it infeasible to adjust for a post-treatment confounder of the mediator-outcome relationship due to incomplete information: it is observed under the actual treatment condition while missing under the counterfactual treatment condition. This study proposes a new… Expand

Figures and Tables from this paper

Why Does a Growth Mindset Intervention Impact Achievement Differently across Secondary Schools? Unpacking the Causal Mediation Mechanism from a National Multisite Randomized Experiment
Abstract The growth mindset or the belief that intelligence is malleable has garnered significant attention for its positive association with academic success. Several recent randomized trials,Expand

References

SHOWING 1-10 OF 27 REFERENCES
Effect Decomposition in the Presence of Treatment-induced Confounding: A Regression-with-residuals Approach
Abstract Analyses of causal mediation are often complicated by treatment-induced confounders of the mediator-outcome relationship. In the presence of such confounders, the natural direct and indirectExpand
Ratio-of-Mediator-Probability Weighting for Causal Mediation Analysis in the Presence of Treatment-by-Mediator Interaction
TLDR
This article shows how the ratio-of-mediator-probability weighting (RMPW) method can be used to decompose total effects into natural direct and indirect effects in the presence of treatment-by-mediators interactions. Expand
Weighting-Based Sensitivity Analysis in Causal Mediation Studies
TLDR
A weighting-based approach to sensitivity analysis for causal mediation studies is presented, extending the ratio-of-mediator-probability weighting method for identifying natural indirect effect and natural direct effect and captures the role of the confounder that contributes to the bias. Expand
Ratio of Mediator Probability Weighting for Estimating Natural Direct and Indirect Effects
Decomposing a total causal effect into natural direct and indirect effects is central to revealing causal mechanisms. Conventional methods achieve the decomposition by specifying an outcome model asExpand
Interventional Effects for Mediation Analysis with Multiple Mediators.
TLDR
Interestingly, the proposed effects capture the path-specific effects of an exposure on an outcome that are mediated by distinct mediators, even when-as often-the structural dependence between the multiple mediators is unknown, for instance, when the direction of the causal effects between the mediator isunknown, or there may be unmeasured common causes of the mediators. Expand
Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis.
TLDR
A general semiparametric framework for obtaining inferences about so-called marginal natural direct and indirect causal effects, while appropriately accounting for a large number of pre-exposure confounding factors for the exposure and the mediator variables is developed. Expand
Effect decomposition in the presence of an exposure-induced mediator-outcome confounder.
TLDR
Three alternative approaches to effect decomposition are described that give quantities that can be interpreted as direct and indirect effects and thatCan shed insight into pathways and questions of mediation even when an exposure-induced mediator-outcome confounder is present. Expand
IDENTIFYING CAUSAL MECHANISMS (PRIMARILY) BASED ON INVERSE PROBABILITY WEIGHTING
SUMMARY This paper demonstrates the identification of causal mechanisms of a binary treatment under selection on observables, (primarily) based on inverse probability weighting; i.e. we consider theExpand
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining theExpand
A simple unified approach for estimating natural direct and indirect effects.
TLDR
A simple procedure based on marginal structural models that directly parameterize the natural direct and indirect effects of interest is introduced and has the advantage that it can be conducted in standard software. Expand
...
1
2
3
...