Doubly robust estimators for generalizing treatment effects on ...

Some Doubly and Multiply Robust Estimators of Controlled Direct ...

(2009) proposed a doubly robust estimator of the. CDE that depends on correct specification of (a) a model for treatment assignment, and (b) ...

Inverse probability of treatment weighting with generalized linear ...

Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully ...

Doubly robust matching estimators for high dimensional ...

Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the ...

Estimating the Average Treatment Effect (ATE) - SAS Help Center

The CAUSALTRT procedure can estimate the average treatment effect (ATE) by using inverse probability weighting, regression adjustment, and doubly robust ...

Doubly Robust Estimation with the R Package drgee - De Gruyter

This is often done by fitting a restricted mean model for the outcome, as in generalized linear models (GLMs) and in generalized estimating equations (GEEs). If ...

Doubly Robust Estimation and Semiparametric Efficiency in ... - MDPI

We investigate a semiparametric generalized partially linear regression model that accommodates missing outcomes, with some covariates modeled ...

Doubly Robust Inference in Causal Latent Factor Models

This estimator leverages information on both the outcome process and the treatment assignment mechanism under a latent factor framework. It ...

Gimme a robust estimator - and make it a double! - Stitch Fix

Another way to tackle this is to do inverse probability of treatment weighting, where you focus your regression efforts on learning the ...

PAC Style Guarantees for Doubly Robust Generalized Front-Door ...

Doubly robust estimators present a promising methodology for estimating treatment effects in observational studies. This paper provides a finite.

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 ...

An Introduction to the Augmented Inverse Propensity Weighted ...

improvements on such double-robust estimators, we refer the reader to Kang and Schafer ... Nonparametric estimation of average treatment effects under exogeneity: ...

Get doubly robust estimates of average treatment effects. in grf - rdrr.io

In the case of a causal forest with continuous treatment, we provide estimates of the average partial effect, i.e., E[Cov[W, Y | X] / Var[W | X]]. In the case ...

Doubly Robust Difference-in-Differences Estimators

This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in ... (generic) nonlinear generalized method ...

"Generalizing trial evidence to target populations in non-nested ...

... doubly robust estimators. We apply these methods to generalize the average treatment effects from two ACTG trials to specified target populations and ...

Estimation of Average Treatment Effects

Their method, called the augmented inverse probability weighting (AIPW) method, is doubly robust (DR) in the sense that it renders consistent estimates of ATEs ...

An Efficient Doubly-Robust Test for the Kernel Treatment Effect

estimation at n−1/4 rates), the asymptotic variance of the AIPW estimator is minimized forˆθ1 = θ1,. ˆθ0 = θ0, thus the IPW estimator is generally dominated by ...

Estimating Effects After Weighting

... estimate potential outcomes and treatment effects ... Treatment Weighting with Generalized Linear Outcome Models for Doubly Robust Estimation.

Doubly Robust Estimation of Local Average Treatment Effects Using ...

Unlike previous approaches, our doubly robust (DR) estimation procedures use quasi-likelihood methods weighted by the inverse of the IV propensity score - so- ...

Advanced introduction to treatment effects for observational data

The double-robust property says that if either the outcome model or the treatment model is correctly specified, we can consistently estimate the effects. The ...

Nonparametric Methods for Doubly Robust Estimation of Continuous ...

In this section we develop doubly robust estimators of the effect ... (2015) Uniformly semiparametric efficient estimation of treatment effects ...