Proximal Causal Inference with Hidden Mediators: Front-Door and Related Mediation Problems

• URL: http://arxiv.org/abs/2111.02927v1
• Date: Thu, 4 Nov 2021 15:04:26 GMT
• Title: Proximal Causal Inference with Hidden Mediators: Front-Door and Related Mediation Problems
• Authors: AmirEmad Ghassami, Ilya Shpitser, Eric Tchetgen Tchetgen
• Abstract summary: Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders. We establish a new hidden front-door criterion which extends the classical front-door result to allow for hidden mediators for which proxies are available. We show that identification of certain causal effects remains possible even in settings where challenges in (i) and (ii) might co-exist.
• Score: 18.84623320851991
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal approach to settings where identification of causal effects hinges upon a set of mediators which unfortunately are not directly observed, however proxies of the hidden mediators are measured. Specifically, we establish (i) a new hidden front-door criterion which extends the classical front-door result to allow for hidden mediators for which proxies are available; (ii) We extend causal mediation analysis to identify direct and indirect causal effects under unconfoundedness conditions in a setting where the mediator in view is hidden, but error prone proxies of the latter are available. We view (i) and (ii) as important steps towards the practical application of front-door criteria and mediation analysis as mediators are almost always error prone and thus, the most one can hope for in practice is that our measurements are at best proxies of mediating mechanisms. Finally, we show that identification of certain causal effects remains possible even in settings where challenges in (i) and (ii) might co-exist.

Related papers

• Criticality and Safety Margins for Reinforcement Learning [53.10194953873209]
  We seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users. We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions. We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality.
  arXiv  Detail & Related papers  (2024-09-26T21:00:45Z)
• Automating the Selection of Proxy Variables of Unmeasured Confounders [16.773841751009748]
  We extend the existing proxy variable estimator to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. We propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects.
  arXiv  Detail & Related papers  (2024-05-25T08:53:49Z)
• Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations [62.48505112245388]
  We take an in-depth look at the causal awareness of modern representations of agent interactions. We show that recent representations are already partially resilient to perturbations of non-causal agents. We propose a metric learning approach that regularizes latent representations with causal annotations.
  arXiv  Detail & Related papers  (2023-12-07T18:57:03Z)
• Partial Identification of Causal Effects Using Proxy Variables [19.23377338970307]
  Proximal causal inference is a recently proposed framework for evaluating causal effects in the presence of unmeasured confounding. In this paper, we propose partial identification methods that do not require completeness and obviate the need for identification of a bridge function.
  arXiv  Detail & Related papers  (2023-04-10T04:18:27Z)
• 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)
• Neighborhood Adaptive Estimators for Causal Inference under Network Interference [152.4519491244279]
  We consider the violation of the classical no-interference assumption, meaning that the treatment of one individuals might affect the outcomes of another. To make interference tractable, we consider a known network that describes how interference may travel. We study estimators for the average direct treatment effect on the treated in such a setting.
  arXiv  Detail & Related papers  (2022-12-07T14:53:47Z)
• Fairness and robustness in anti-causal prediction [73.693135253335]
  Robustness to distribution shift and fairness have independently emerged as two important desiderata required of machine learning models. While these two desiderata seem related, the connection between them is often unclear in practice. By taking this perspective, we draw explicit connections between a common fairness criterion - separation - and a common notion of robustness.
  arXiv  Detail & Related papers  (2022-09-20T02:41:17Z)
• Inferring Effect Ordering Without Causal Effect Estimation [1.6114012813668932]
  Predictive models are often employed to guide interventions across various domains, such as advertising, customer retention, and personalized medicine. Our paper addresses the question of when and how these predictive models can be interpreted causally. We formalize two assumptions, full latent mediation and latent monotonicity, that are jointly sufficient for inferring effect ordering without direct causal effect estimation.
  arXiv  Detail & Related papers  (2022-06-25T02:15:22Z)
• Partial Identification with Noisy Covariates: A Robust Optimization Approach [94.10051154390237]
  Causal inference from observational datasets often relies on measuring and adjusting for covariates. We show that this robust optimization approach can extend a wide range of causal adjustment methods to perform partial identification. Across synthetic and real datasets, we find that this approach provides ATE bounds with a higher coverage probability than existing methods.
  arXiv  Detail & Related papers  (2022-02-22T04:24:26Z)
• Nested Counterfactual Identification from Arbitrary Surrogate Experiments [95.48089725859298]
  We study the identification of nested counterfactuals from an arbitrary combination of observations and experiments. Specifically, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones.
  arXiv  Detail & Related papers  (2021-07-07T12:51:04Z)

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.