Double Robust Efficient Estimators of Longitudinal Treatment Effects

A number of sophisticated estimators of longitudinal effects have been proposed for estimating the intervention-specific mean outcome.

Available estimators for longitudinal causal effects differ in their efficiency, in their nuisance parameters (and their choice of estimator of the nuisance ...

Double Robust Efficient Estimators of Longitudinal Treatment Effects

Comparisons of various approaches to estimating a causal effect in a longitudinal treatment setting using both simulated data and data measured from a human ...

Double Robust Efficient Estimators of Longitudinal Treatment Effects

Under full misspecification, the bias of the double robust estimators remained better than that of the inverse propensity estimator under misspecification, but ...

Double Robust Efficient Estimators of Longitudinal Treatment Effects

Double Robust Efficient Estimators of Longitudinal Treatment Effects: Comparative Performance in Simulations and a Case Study ... To read the full-text of this ...

Double Robust Efficient Estimators of Longitudinal Treatment Effects ...

Double Robust Efficient Estimators of Longitudinal Treatment Effects: Comparative Performance in Simulations and a Case Study. Linh Tran, C. Yiannoutsos, K ...

Double Robust Efficient Estimators of Longitudinal Treatment Effects

Double Robust Efficient Estimators of Longitudinal Treatment Effects: Comparative Performance in Simulations and a Case Study. Int. J. Biostat. Pub Date ...

Doubly robust estimation in causal inference with missing outcomes

The proposed doubly robust estimators can correct these two types of bias and provide protection against model misspecification. The consistency and asymptotic ...

Implementing Double-robust Estimators of Causal Effects

For example, when treatment is binary, we can use a logistic or probit model with the baseline variables as covariates and take the predicted value from the ...

Doubly robust estimation and causal inference in longitudinal ...

Motivated by aging research, we propose an estimator of the effect of a time-varying exposure on an outcome in longitudinal studies with dropout and truncation ...

Doubly Robust Estimation of Average Treatment ... - Project MUSE

We explain how such parameters can be defined through parameters in a marginal structural (working) model. We illustrate how existing software can be used for ...

Doubly robust estimators for the average treatment effect under ...

We define the target parameter mapping as θ(P) = EP{m(W)}. 2.1 Existing estimators and asymptotic properties. Doubly robust and efficient estimation of θ in the ...

Double robust estimation in longitudinal marginal structural models ...

The benefits of taking a double robust approach when estimating an average treatment effect in survival analysis with longitudinal data are discussed in Yu ...

University of California, Berkeley

Double Robust Estimation in Longitudinal. Marginal Structural Models. Zhuo Yu ... partial likelihood, then the double robust estimator is typically more efficient ...

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.

Combining Doubly Robust Methods and Machine Learning ... - arXiv

... doubly robust estimators (typically revealed in settings of heterogeneous treatment effects). ... longitudinal eval- uation of costs and treatments ...

Double Robust, Flexible Adjustment Methods for Causal Inference

Robust Efficient Estimators of Longitudinal Treatment Effects: Comparative Performance in Simulations ... Double robust estimation in longitudinal marginal ...

Doubly Robust Estimation of Causal Effects - Oxford Academic

Doubly robust estimation combines 2 approaches to estimating the causal effect of an exposure (or treatment) on an outcome. We examine in ...

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 ...

Good reading on when "doubly robust" is worth the effort?

The need for doubly-robust estimators with cross-fitting when using data-adaptive machine learning for nuisance function estimation arises from ...