Causal Mediation Analysis with Hidden Confounders

• URL: http://arxiv.org/abs/2102.11724v1
• Date: Sun, 21 Feb 2021 06:46:11 GMT
• Title: Causal Mediation Analysis with Hidden Confounders
• Authors: Lu Cheng, Ruocheng Guo, Huan Liu
• Abstract summary: Causal mediation analysis (CMA) is a formal statistical approach for identifying and estimating causal effects. This work 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.
• Score: 24.246450472404614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Identification of Average Causal Effects in Confounded Additive Noise Models [7.064432289838905]
  We introduce a novel approach for estimating the average causal effects (ACEs) of any subset of the treatment variables on the outcome. We also propose a randomized algorithm that further reduces the number of required interventions to poly-logarithmic in the number of nodes. This establishes that a poly-logarithmic number of interventions is sufficient to infer the causal effects of any subset of treatments on the outcome in confounded ANMs with high probability.
  arXiv  Detail & Related papers  (2024-07-13T21:46:57Z)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Sharp Bounds for Generalized Causal Sensitivity Analysis [30.77874108094485]
  We propose a unified framework for causal sensitivity analysis under unobserved confounding. This includes (conditional) average treatment effects, effects for mediation analysis and path analysis, and distributional effects. Our bounds for (conditional) average treatment effects coincide with recent optimality results for causal sensitivity analysis.
  arXiv  Detail & Related papers  (2023-05-26T14:44:32Z)
• Disentangled Representation for Causal Mediation Analysis [25.114619307838602]
  Causal mediation analysis is a method that is often used to reveal direct and indirect effects. Deep learning shows promise in mediation analysis, but the current methods only assume latent confounders that affect treatment, mediator and outcome simultaneously. We propose the Disentangled Mediation Analysis Variational AutoEncoder (DMAVAE), which disentangles the representations of latent confounders into three types to accurately estimate the natural direct effect, natural indirect effect and total effect.
  arXiv  Detail & Related papers  (2023-02-19T23:37:17Z)
• Probabilistic Variational Causal Approach in Observational Studies [0.0]
  We introduce a new causal methodology that accounts for the rarity and frequency of events in observational studies based on their relevance to the underlying problem. We propose a direct causal effect metric called the Probabilistic Variational Causal Effect (PACE) and its variations adhering to certain postulates applicable to both non-binary and binary treatments.
  arXiv  Detail & Related papers  (2022-08-12T13:34:17Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• Causal Effect Estimation using Variational Information Bottleneck [19.6760527269791]
  Causal inference is to estimate the causal effect in a causal relationship when intervention is applied. We propose a method to estimate Causal Effect by using Variational Information Bottleneck (CEVIB)
  arXiv  Detail & Related papers  (2021-10-26T13:46:12Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)
• Learning Decomposed Representation for Counterfactual Inference [53.36586760485262]
  The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. We propose a synergistic learning framework to 1) identify confounders by learning representations of both confounders and non-confounders, 2) balance confounder with sample re-weighting technique, and simultaneously 3) estimate the treatment effect in observational studies via counterfactual inference.
  arXiv  Detail & Related papers  (2020-06-12T09:50:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.