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This document provides information and documentation for the SAS macro used for doubly robust estimation, requiring Base SAS® Software version 8.02 and later. It includes related files and instructions
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How to fill out SAS® Macro for Doubly Robust Estimation, v0.90

01
Download the SAS® Macro for Doubly Robust Estimation, v0.90 from the official source.
02
Open your SAS environment and ensure that you have access to the necessary libraries.
03
Load the macro into your SAS session using the %INCLUDE statement.
04
Prepare your dataset with the necessary covariates and outcome variable for analysis.
05
Define the treatment variable as well as any potential confounding variables in your dataset.
06
Call the macro by providing the relevant input parameters, including treatment and confounder variables.
07
Execute the macro and review the output for estimates and diagnostics.
08
Adjust the parameters as necessary based on the output for further analysis.

Who needs SAS® Macro for Doubly Robust Estimation, v0.90?

01
Researchers conducting observational studies that require robust causal inference.
02
Data analysts looking for methods to estimate treatment effects under both model and propensity score approaches.
03
Statisticians interested in advanced estimation techniques for treatment effects.
04
Healthcare professionals evaluating the impact of interventions in clinical studies.
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SAS® Macro for Doubly Robust Estimation, v0.90 is a statistical tool designed to provide an estimation method that combines propensity score modeling with outcome regression. It aims to improve the robustness of causal estimates in observational studies.
Researchers and statisticians conducting observational studies that require causal inference should file SAS® Macro for Doubly Robust Estimation, v0.90, especially those employing propensity score methods.
To fill out the macro, users must input relevant dataset variables, specify the treatment and covariates, and define the model parameters according to the guidelines provided in the macro documentation.
The purpose of the macro is to facilitate the estimation of treatment effects in observational data by providing a doubly robust approach, ensuring more reliable causal inferences even when one of the modeling assumptions is violated.
Users must report variables used in the propensity score model, outcome variable, treatment assignment, the model parameters, and results of the estimation, including confidence intervals and significance levels.
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