Plot causal prediction outputs

S3 method for visualizing causal predictions from predict.causalmixgpd_causal_fit(). For mean/quantile, plots treated/control and treatment effect versus PS (or index). For type = "sample", plots arm-level posterior predictive samples alongside treatment-effect samples. For density/prob, plots treated/control values versus y.

Usage

# S3 method for class 'causalmixgpd_causal_predict'
plot(x, y = NULL, ...)

Arguments

x

Object of class causalmixgpd_causal_predict.

y

Ignored.

...

Additional arguments passed to ggplot2 functions.

Value

A ggplot object or a list of ggplot objects.

Details

The causal prediction object carries arm-specific predictions together with the implied contrast. For mean predictions, the contrast is \(m_1(x) - m_0(x)\). For quantile predictions, the contrast is \(Q_{Y^1}(\tau \mid x) - Q_{Y^0}(\tau \mid x)\). The plotting method keeps those arm and contrast views synchronized.

Unlike plot.causalmixgpd_causal_fit(), which diagnoses MCMC behavior inside the outcome models, this method visualizes predictive quantities after posterior integration. It is therefore the natural plotting method once the user has already accepted the fitted-model diagnostics.