Introduction

This blog is a new function, treatment_model that have been added to the Dyn4cast package. The function provides means for enhanced estimation of propensity score and treatments effects from randomized controlled designed experiments.

Observational study involves the evaluation of outcomes of participants not randomly assigned treatments or exposures. To be able to assess the effects of the outcome, the participants are matched using propensity scores (PSM). This then enables the determination of the effects of the treatments on those treated against those who were not treated. Most of the earlier functions available for this analysis only enables the determination of the average treatments effects on the treated (ATT) while the other treatment effects are optional. This is where this functions is unique because five different average treatment effects are estimated simultaneously, in spite of the one line code arguments. The five treatment effects are:

Average treatment effect for the entire (ATE) population

Average treatment effect for the treated (ATT) population

Average treatment effect for the controlled (ATC) population

Average treatment effect for the evenly matched (ATM) population

Average treatment effect for the overlap (ATO) population.

There excellent materials dealing with each of the treatment effects, please see

The basic usage of the codes are:

treatment_model(Treatment, x_data)

Arguments

Treatment Vector of binary data (0, 1) LHS for the treatment effects estimation

x_data Data frame of explanatory variables for the RHS of the estimation

Let us go!

Load library

library(Dyn4cast)

Estimation of treatment effects model

Treatment <- treatments$treatment
data <- treatments[, c(2:3)]
treat <- treatment_model(Treatment, data)

summary(treat)
 Length Class Mode
P_score 500 -none- numeric
Effect 5 data.frame list
Fitted_estimate 500 -none- numeric
Residuals 500 -none- numeric
Model 30 glm list
Experiment plot 11 gg list
ATE plot 11 gg list
ATT plot 11 gg list
ATC plot 11 gg list
ATM plot 11 gg list
ATO plot 11 gg list
weights 5 data.frame list 

treat$Effect
 ATE ATT ATC ATM ATO
1 2.465581 4.481578 -0.2272903 1.968055 2.056329

Estimated propensity scores from the model

head(treat$P_score)
 1 2 3 4 5 6
0.98347730 0.97153060 0.63093981 0.69944426 0.07971976 0.90935715 

##Ffitted values from the model

head(treat$Fitted_estimate)
 1 2 3 4 5 6
0.98347730 0.97153060 0.63093981 0.69944426 0.07971976 0.90935715 

tail(treat$Residuals)
 495 496 497 498 499 500
-2.250645 -1.757826 1.402062 1.010765 -1.151789 1.898509 

treat$`ATE plot`

treat$`ATT plot`

treat$`ATM plot`