Implementing TMLE in the Presence of a Continuous Outcome
1 Citation
2 A Real data: Box 14: Initial data setup
3 Box 1: Transformation of the continuous outcome variable
4 Box 2: Fit SuperLearner & make predictions
5 Box 3a & 3b: Get predictions under both treatments, \(A = 0\) and \(A = 1\)
6 Box 4: Get initial treatment effect estimate
7 Box 5: Fit propensity score SuperLearner & make predictions
8 Box 6: Estimate clever covariate \(H\)
9 Box 7: Estimate fluctuation parameter \(\epsilon\)
10 Box 8: Update the initial outcome model prediction based on targeted adjustment of the initial predictions using the PS model
11 Box 9: Find treatment effect estimate
12 Box 10: Transform back the treatment effect estimate in the original outcome scale
13 Box 11: Confidence interval estimation
14 Box 12: tmle package
15 Box 13: Comparison with Keele (2021)
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Implementing TMLE in the Presence of a Continuous Outcome

Section 6 Box 4: Get initial treatment effect estimate

ObsData$Pred.TE <- ObsData$Pred.initialQ0.Y1 - ObsData$Pred.initialQ0.Y0   
mean(ObsData$Pred.TE)

## [1] 0.007043316

hist(ObsData$Pred.TE)