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An important problem in causal inference is to break down the total effect of treatment into different causal pathways and quantify the causal effect in each pathway. Causal mediation analysis (CMA) is a formal statistical approach for identifying and estimating these causal effects. Central to CMA is the sequential ignorability assumption that implies all pre-treatment confounders are measured and they can capture different types of confounding, e.g., post-treatment confounders and hidden confounders. Typically unverifiable in observational studies, this assumption restrains both the coverage and practicality of conventional methods. This work, therefore, aims to circumvent the stringent assumption by following a causal graph with a unified confounder and its proxy variables. Our core contribution is an algorithm that combines deep latent-variable models and proxy strategy to jointly infer a unified surrogate confounder and estimate different causal effects in CMA from observed variables. Empirical evaluations using both synthetic and semi-synthetic datasets validate the effectiveness of the proposed method.
Causal mediation analysis is used to evaluate direct and indirect causal effects of a treatment on an outcome of interest through an intermediate variable or a mediator.It is difficult to identify the direct and indirect causal effects because the me
Interventional effects for mediation analysis were proposed as a solution to the lack of identifiability of natural (in)direct effects in the presence of a mediator-outcome confounder affected by exposure. We present a theoretical and computational s
Greater understanding of the pathways through which an environmental mixture operates is important to design effective interventions. We present new methodology to estimate the natural direct effect (NDE), natural indirect effect (NIE), and controlle
Causal variance decompositions for a given disease-specific quality indicator can be used to quantify differences in performance between hospitals or health care providers. While variance decompositions can demonstrate variation in quality of care, c
The last decade witnessed the development of algorithms that completely solve the identifiability problem for causal effects in hidden variable causal models associated with directed acyclic graphs. However, much of this machinery remains underutiliz