change simulation

sessions/causal-mediation-analysis-estimation.qmd

@@ -612,8 +612,11 @@ Let's use g-computation!
 A1Data <- A0Data <- ObsData
 # In one world A equals 1 for everyone, in the other one it equals 0 for everyone
 # The rest of the data stays as is (for now)
-A1Data$A <- 1; A0Data$A <- 0
-head(ObsData); head(A1Data); head(A0Data)exp(Q$coef[2])
+A1Data$A <- 1
+A0Data$A <- 0
+head(ObsData)
+head(A1Data)
+head(A0Data)
 # Predict Y if A=1
 Y_A1 <- predict(Q, A1Data, type="response") 
 # Predict Y if A=0
@@ -622,19 +625,20 @@ Y_A0 <- predict(Q, A0Data, type="response")
 data.frame(Y_A1=head(Y_A1), Y_A0=head(Y_A0), TRUE_Y=head(ObsData$Y)) |> round(digits = 2)
 
 # Mean outcomes in the two worlds
-pred_A1 <- mean(Y_A1); pred_A0 <- mean(Y_A0)
+pred_A1 <- mean(Y_A1)
+pred_A0 <- mean(Y_A0)
 # Marginal odds ratio
 MOR_gcomp <- (pred_A1*(1 - pred_A0))/((1 - pred_A1)*pred_A0)
 # ATE (risk difference)
 RD_gcomp <- pred_A1 - pred_A0
-c(MOR_gcomp, RD_gcomp) |> round(digits=3)
+c(MOR_gcomp, RD_gcomp) |> round(digits=2)
 ```
 
 The demo shows how to estimate total effect using g-computation. If you
 would like to estimate PNDE, TNIT, PNIE, TNDE, then you also need to
 predict two potential mediators observed in a hypothetical world in
 which all individuals are intervention (A=1) or non-intervention (A=0).
-We recommend take the advatange of R packages to do the work :)
+We recommend take the advantage of R packages to do the work :)
 
 ```{r}
 set.seed(1)