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The approach focuses on estimating the effect of fluctuations in a treatment variable on an outcome.
Only the treatment variable of perfusing solution was found to be significant (p<0.05).
The design used between-subjects variation and involved information feedback as the treatment variable.
We consider estimation and inference on average treatment effects under unconfoundedness conditional on the realizations of the treatment variable and covariates.
Afterwards, a quasi-experimental design was employed, with day-or-night video of the view as the treatment variable.
This article discusses and compares several methods for estimating the parameters of a latent regression model when one of the explanatory variables is an endogenous binary (treatment) variable.
The treatment variable is area of residence, rural vs. urban.
cause or treatment variable X: an independent variable that influences the values of another variable.
With the gpscore programme, the study first took a logarithmic transformation of the treatment variable.
Section 3 describes our data and construction of the key treatment variable.
where treatment group is either the binary treatment variable or the intensity treatment.
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