- Sufficient dimension reduction for feasible and robust estimation of ...🔍
- Optimal Doubly Robust Estimation of Heterogeneous Causal Effects🔍
- Stratified Doubly Robust Estimators for the Average Causal Effect🔍
- Doubly robust estimation of the local average treatment effect curve🔍
- Doubly robust estimation in missing data and causal inference models.🔍
- Doubly Robust Estimation of Causal Effects with Multivalued ...🔍
- The CAUSALTRT Procedure🔍
- Doubly robust estimators for generalizing treatment effects on ...🔍
Doubly Robust Estimation of Causal Effect
Sufficient dimension reduction for feasible and robust estimation of ...
We then integrate our results using imputation, inverse probability weighting, and doubly robust augmentation estimators. Doubly robust estimators are locally ...
Optimal Doubly Robust Estimation of Heterogeneous Causal Effects
Abstract: Heterogeneous effect estimation plays a crucial role in causal inference, with applications across medicine and social science. Many ...
Stratified Doubly Robust Estimators for the Average Causal Effect
Key WORDS: Confounding lactor; Observational study; propensity score; btratihcation. 1. Introduction. It is often of primary interest to estimate the treatment ...
We want to know the causal effect of colonoscopy (D) on colorectal cancer (Y ). High rates of noncompliance for colonoscopy.
Doubly robust estimation in missing data and causal inference models.
In a missing data model, an estimator is DR if it remains consistent when either (but not necessarily both) a model for the missingness mechanism or a model for ...
Doubly Robust Estimation of Causal Effects with Multivalued ...
This paper provides doubly robust estimators for treatment effect parameters which are defined in multivalued treatment effect framework.
The CAUSALTRT Procedure - SAS Support
methods to estimate the causal effect; doubly robust estimation methods provide unbiased estimates even if one of the models is misspecified. Whenever your ...
Doubly robust estimators for generalizing treatment effects on ...
The proposed estimator is doubly robust in the sense that it is consistent for the target population estimands if either the survival model or ...
The Causal Cookbook: Recipes for Propensity Scores, G ...
However, causal effects are not limited to marginal effects; they can also be so-called conditional effects (Box 1). We focus on the marginal ...
teffects intro — Introduction to treatment effects for observational data
... estimates of the treatment effect. Stata's teffects command implements two doubly robust estimators, the augmented inverse- probability-weighted (AIPW) ...
Relaxed doubly robust estimation in causal inference
It operates in a manner reminiscent of the traditional doubly robust estimator but with a reduced requirement for double robustness. In essence, it only ...
Is double machine learning doubly robust? If so, how?
The need for doubly-robust estimators with cross-fitting when using data-adaptive machine learning for nuisance function estimation arises from ...
Covariate selection with group lasso and doubly robust estimation of ...
Keyphrases · Causal Effect · Least Absolute Shrinkage and Selection Operator (LASSO) · Doubly Robust Methods · Outcome Model · Treatment Model · Covariate Selection ...
Multiply Robust Estimation of Causal Effects under Principal ... - OUCI
AbstractCausal inference concerns not only the average effect of the treatment on the outcome but also the underlying mechanism through an intermediate ...
G-computation, propensity score-based methods, and targeted ...
In our study, we sought to compare different sets of covariates to consider to estimate a marginal causal effect. Moreover, we compared GC, PS- ...
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 ...
Doubly Robust Causal Effect Estimation under Networked ...
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6.5 - Doubly Robust Methods, Matching, Double Machine ... - YouTube
In this part of the Introduction to Causal Inference course, we sketch out a few other methods for causal effect estimation: doubly robust ...
Exploring modern machine learning methods to improve causal ...
Funk MJ, Westreich D, Wiesen C, Stürmer T, Brookhart MA, and Davidian M (2011). Doubly robust estimation of causal effects. The American Journal ...
lmtp: An R Package for Estimating the Causal Effects of Modified ...
The sequential doubly-robust estimator is based on a unbiased transformation of the efficient influence function of the target estimand. For a ...