CausalPCA.ContCont function

Show a causal diagram of the median correlation between the counterfactuals in the continuous-continuous setting

This function provides a diagram that depicts the estimable correlations $\rho(_{T_0, S})$ and $\rho(_{T_1, S})$, and median of the correlation $\rho(_{T_0, T_1})$ for a specified range of values of the predictive causal association (PCA; $\rho_{\psi}$).

CausalPCA.ContCont(x, Min=-1, Max=1, Cex.Letters=3, Cex.Corrs=2, 
Lines.Rel.Width=TRUE, Col.Pos.Neg=TRUE)

Arguments

• x: An object of class PCA.ContCont. See PCA.ContCont.
• Min: The minimum values of the PCA that should be considered. Default=$-1$.
• Max: The maximum values of the PCA that should be considered. Default=$1$.
• Cex.Letters: The size of the symbols for $S$, $T_{0}$, and $T_{1}$ in the diagram. Default=$3$.
• Cex.Corrs: The size of the text depicting the (median) correlations in the diagram. Default=$2$.
• Lines.Rel.Width: Logical. When Lines.Rel.Width=TRUE, the widths of the lines that represent the correlations in the diagram are relative to the size of the correlations (i.e., a smaller line is used for correlations closer to zero whereas a thicker line is used for (absolute) correlations closer to $1$). When Lines.Rel.Width=FALSE, the width of all lines representing the correlations between the counterfactuals is identical. Default=TRUE.
• Col.Pos.Neg: Logical. When Col.Pos.Neg=TRUE, the color of the lines that represent the correlations in the diagram is red for negative correlations and black for positive ones. When Col.Pos.Neg=FALSE, all lines are in black. Default=TRUE.

References

Alonso, A., Van der Elst, W., & Molenberghs, G. (submitted). Validating predictors of therapeutic success: a causal inference approach.

Author(s)

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

See Also

PCA.ContCont

Examples

# Generate the vector of PCA.ContCont values when rho_T0S=.3, rho_T1S=.9, 
# sigma_T0T0=2, sigma_T1T1=2,sigma_SS=2, and the grid of values {-1, -.99, 
# ..., 1} is considered for the correlations between T0 and T1:
PCA <- PCA.ContCont(T0S=.3, T1S=.9, T0T0=2, T1T1=2, SS=2, 
T0T1=seq(-1, 1, by=.01))

# Obtain causal diagram for PCA score range [-1; 1]:
CausalPCA.ContCont(PCA,  Min=-1, Max=1)

# Obtain causal diagram for PCA score range [0.5; 1]:
CausalPCA.ContCont(PCA,  Min=0.5, Max=1)