- Doubly Robust Causal Effect Estimation under Networked ...🔍
- Double Robust🔍
- Doubly Robust Estimation of Causal Effects with Multivalued ...🔍
- Doubly robust matching estimators for high dimensional ...🔍
- Double|robust treatment effects🔍
- Doubly Robust Estimation of Causal Effect🔍
- Doubly robust estimation of causal effects.🔍
- R package for doubly robust estimates of causal effects in ...🔍
Doubly Robust Estimation of Causal Effect
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 ...
Double Robust, Flexible Adjustment Methods for Causal Inference
In causal inference, functional form misspecification of underlying models can bias estimates of treatment effects (Hernán & Robins, 2020; Morgan & Winship, ...
Doubly Robust Estimation of Causal Effects with Multivalued ...
doubly robust estimation of causal effects with multivalued treatments: an application to the returns to schooling (replication data) ... This paper provides ...
Doubly robust matching estimators for high dimensional ...
There is some controversy regarding causal estimates of immutable characteristics such as gender. While there exist studies aiming to estimate the causal effect ...
Double-robust treatment effects - Stata News
Conditional independence allows us to use differences in model-adjusted averages to estimate the ATE. The regression-adjustment (RA) estimator uses a model for ...
EconPapers: Doubly Robust Estimation of Causal Effects ... - RePEc
Abstract: This paper provides doubly robust estimators for treatment effect parameters which are defined in multivalued treatment effect framework. We apply ...
Doubly Robust Estimation of Causal Effect - ResearchGate
Download Citation | Doubly Robust Estimation of Causal Effect: Upping the Odds of Getting the Right Answers | Propensity score-based methods or multiple ...
Doubly Robust Estimation of Causal Effect
We describe in this paper a doubly robust estimator which combines both models propitiously to offer analysts two chances for obtaining a valid causal estimate, ...
Doubly robust estimation of causal effects. - Abstract - Europe PMC
Doubly robust estimation combines 2 approaches to estimating the causal effect of an exposure (or treatment) on an outcome. We examine in ...
R package for doubly robust estimates of causal effects in ... - GitHub
R package for doubly robust estimates of causal effects in high-dimensions using flexible Bayesian methods - jantonelli111/DoublyRobustHD.
Doubly robust treatment effect estimation with missing attributes
Received October 2019; revised May 2020. Key words and phrases. Missing data, causal inference, potential outcomes, observational data, propensity score ...
Multiply robust estimation of causal effects under principal ignorability
These results extend the classic doubly robust estimators for the average causal effect in observational studies (Bang & Robins,. 2005) and are ...
Doubly Robust Estimation of Causal Effects with Multivalued ... - jstor
DOUBLY ROBUST ESTIMATION OF CAUSAL EFFECTS WITH. MULTIVALUED TREATMENTS: AN APPLICATION TO THE. RETURNS TO SCHOOLING. S. DERYA UYSAL. Department ...
Estimating causal effects under sparsity using the econml package
Doubly robust estimation involves fitting two models: one model to predict the treatment X from possible confounders, and another model to ...
Doubly robust estimation of the causal effects in the causal inference ...
In the causal inference with missing outcome data, an estimator is doubly robust if it remains consistent and asymptotically normal (CAN) when ...
Doubly robust estimation of causal effects - BibSonomy
Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of ...
Derya Uysal Doubly Robust Estimation of Causal Effects with ...
Abstract This paper provides doubly robust estimators for treatment effect parameters which are defined in a multivalued treatment effect…
Doubly robust estimation in causal inference with missing outcomes
Abstract. Estimation of the average treatment effect (ATE) and the average treatment effect on the treated (ATT) are two important topics of ...
A General Double Robustness Result for Estimating Average ...
Kaiser (2013) has extended this contri- bution to decomposition problems and estimating the average treatment effect on the ... Doubly robust estimation of causal ...
Towards optimal doubly robust estimation of heterogeneous causal ...
Abstract. Heterogeneous effect estimation plays a crucial role in causal inference, with applications across medicine and social science. Many methods for ...