Doubly robust estimation of causal effects.

On the Robustness of Doubly Robust Estimators in Causal Inference

Doubly robust estimation of causal effects. American Journal of Epidemiology,. 173(7):761–767. Glynn, A. N. and Quinn, K. M. (2010). An introduction to the ...

Doubly robust estimation of causal effects implementation

Instead of treating each subject in your analysis as 1 subject, you now treat them as n copies of a subject, where n is their weight. If you run ...

Non-parametric Methods for Doubly Robust Estimation of ...

Continuous treatments (e.g. doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for ...

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

This paper develops a novel doubly robust triple cross-fit estimator to estimate the average treatment effect (ATE) using observational and imaging data.

Average Treatment Effects: Double Robustness - YouTube

Average Treatment Effects: Double Robustness · Stanford Graduate School of Business · Conditional Average Treatment Effects: Overview · Average ...

Doubly robust estimation - (Causal Inference) - Fiveable

Doubly robust estimation is a statistical technique that provides reliable estimates of causal effects by combining two methods: regression adjustment and ...

Doubly Robust Estimation of Causal Effects - University of Sydney

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 ...

Full article: Relaxed doubly robust estimation in causal inference

As mentioned earlier, the traditional doubly robust estimation hinges on the correct specification of the unknown function and the consistent estimation of the ...

A doubly robust approach for impact evaluation of interventions for ...

By emphasizing the potential of doubly robust estimation as a reliable method for estimating the effects of business process interventions without relying on ...

Doubly Robust Inference in Causal Latent Factor Models

Shah: [email protected]. 1. Page 2. There are two widely used approaches for treatment effect estimation: outcome ...

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 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 ...

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 with Multivalued ...

doubly robust estimation of causal effects with multivalued treatments: an application to the returns to schooling (replication data) ... This paper provides ...

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 ...

This paper provides doubly robust estimators for treatment effect parameters which are defined in a multivalued treatment effect framework.

resolving an apparent paradox in doubly robust estimators

Doubly robust estimators are an approach used for estimating causal effects, usually based on fitting 2 statistical models (1).

Doubly Robust Estimation in Missing Data and Causal Inference ...

next consider estimation of the effect of a binary treatment in the presence of high-dimensional baseline covariate data un- der the assumption of no unmeasured ...

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 ...