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Doubly Robust Causal Effect Estimation under Networked ...


Doubly Robust Causal Effect Estimation under Networked ... - arXiv

We propose a novel doubly robust causal effect estimator under networked interference, by adapting the targeted learning technique to the training of neural ...

Doubly Robust Causal Effect Estimation under Networked ...

To answer the above question, we propose a doubly robust estimator, called TNet, for estimating causal effects under networked interference via targeted ...

Doubly Robust Causal Effect Estimation under Networked ... - arXiv

Theorem 4.1. ... Under the no interference assumption, TMLE establishes a three-step estimation for average causal effects to ensure that the ...

(PDF) Doubly Robust Estimation of Causal Effects in Network-Based ...

Doubly robust estimation in observational studies with partial interference: Doubly Robust Estimatio... ... Interference occurs when the treatment (or exposure) ...

Doubly Robust Causal Effect Estimation under Networked ... - Linnk AI

The authors propose a novel doubly robust causal effect estimator under networked interference by adapting the targeted learning technique to the training ...

Nonparametric doubly robust estimation of causal effect on networks ...

Interconnection of nodes takes great challenge to the estimation of causal effect in the network. In this study, we develop a nonparametric ...

Doubly Robust Causal Effect Estimation under Networked ...

TL;DR: The document introduces TNet, a novel causal effect estimator for networked interference, achieving double robustness and improved bias reduction in ...

Doubly Robust Causal Effect Estimation under Networked ...

This paper introduces a doubly robust method for estimating causal effects under network interference. By combining targeted learning with a graph-based ...

(PDF) Doubly Robust Estimation of Causal Effects in Network-Based ...

In settings where some units are exposed to a treatment and its effect spills over connected units, estimating both the direct effect of the treatment and ...

Doubly Robust Estimation of Causal Effects - Oxford Academic

The doubly robust estimator requires us to specify regression models for the outcome and the exposure as a function of covariates. In the case ...

Doubly Robust Causal Effect Estimation under Networked ...

Article,. Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning. W. Chen, R. Cai, ...

Doubly Robust Estimation of Causal Effect | Circulation

We describe in this article a doubly robust estimator which combines both models propitiously to offer analysts 2 chances for obtaining a valid causal estimate.

Doubly Robust Causal Modeling to Evaluate Device Implantation

Doubly robust estimation is a class of statistical methods that can be used to avoid spurious (noncausal) associations between a treatment and outcome.

Doubly Robust Causal Effect Estimation under Networked Interference

The authors propose a novel doubly robust causal effect estimator under networked interference by adapting the targeted learning technique to the training of ...

Doubly Robust Triple Cross-Fit Estimation for Causal Inference with ...

A widely adopted approach to estimating causal effects is the double robust (DR) estimator [2], which effectively addresses potential ...

Doubly Robust Estimation of Causal Effects - Oxford Academic

Doubly robust estimation combines a form of outcome regression with a model for the exposure (ie, the propensity score) to estimate the causal effect of an ...

[PDF] Estimating Causal Effects on Networked Observational Data ...

Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning · Weilin ChenRuichu Cai +4 authors. Zhifeng Hao. Computer Science ...

Semiparametric Bayesian doubly robust causal estimation

DR estimation combines outcome regression (OR) and propensity score (PS) models in such a way that correct specification of just one of two models is enough to ...

Doubly robust estimation of causal effects implementation

Doubly robust estimation is not actually particularly hard to implement in the language of your choice. All you are actually doing is ...

Graph Machine Learning based Doubly Robust Estimator for ...

We address the challenge of inferring causal effects in social network data. This results in challenges due to interference -- where a ...