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Plot QTE-style effect summaries

plot.causalmixgpd_qte.Rd

plot.causalmixgpd_qte() visualizes objects returned by qte, qtt, and cqte. The type parameter controls the plot style. When type is omitted, cqte() objects default to "effect" and, when multiple quantile levels are present, facet_by = "id". Whenever quantile index appears on the x-axis, it is shown as an ordered categorical axis with equidistant spacing:

  • "both" (default): Returns a list with both trt_control (treated vs control quantile curves) and treatment_effect (QTE curve) plots

  • "effect": QTE curve vs quantile levels (probs) with pointwise CI error bars

  • "arms": Treated and control quantile curves vs probs, with pointwise CI error bars

Usage

# S3 method for class 'causalmixgpd_qte'
plot(
  x,
  y = NULL,
  type = c("both", "effect", "arms"),
  facet_by = c("tau", "id"),
  plotly = getOption("CausalMixGPD.plotly", FALSE),
  ...
)

Arguments

x

Object of class causalmixgpd_qte.

y

Ignored.

type

Character; plot type:

  • "both" (default): returns a list with both arm curves and treatment-effect plots

  • "effect": QTE curve with pointwise CI error bars

  • "arms": treated and control quantile curves with pointwise CI error bars

facet_by

Character; faceting strategy when multiple prediction points exist:

  • "tau" (default): facets by quantile level

  • "id": facets by prediction point

plotly

Logical; if TRUE, convert the ggplot2 output to a plotly / htmlwidget representation via .wrap_plotly(). Defaults to getOption("CausalMixGPD.plotly", FALSE).

...

Additional arguments passed to ggplot2 functions.

Value

A list of ggplot objects with elements trt_control and treatment_effect (if type="both"), or a single ggplot object (if type is "effect" or "arms").

Details

The effect view emphasizes the quantile contrast \(\tau \mapsto Q_{Y^1}(\tau) - Q_{Y^0}(\tau)\), while the arms view shows the treated and control quantile functions that generate that contrast. For conditional CQTE objects, faceting can separate covariate profiles so the same quantile contrast is compared across prediction settings.

These graphics visualize posterior summaries of the effect object itself. They are therefore downstream of model fitting and downstream of the causal prediction step.

See also

qte, cqte, summary.causalmixgpd_qte.

Examples

if (FALSE) { # \dontrun{
qte_result <- cqte(fit, probs = c(0.1, 0.5, 0.9), newdata = X_new)
plot(qte_result)  # CQTE default: effect plot (faceted by id when needed)
plot(qte_result, type = "effect")  # single QTE plot
plot(qte_result, type = "arms")    # single arms plot
} # }