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Plot ATE-style effect summaries

plot.causalmixgpd_ate.Rd

plot.causalmixgpd_ate() visualizes objects returned by ate, att, cate, and ate_rmean. The type parameter controls the plot style. When type is omitted, cate() objects default to "effect":

  • "both" (default): Returns a list with both trt_control (treated vs control means) and treatment_effect (ATE curve) plots

  • "effect": ATE curve/points vs index/PS with pointwise CI error bars

  • "arms": Treated mean vs control mean, with pointwise CI error bars

Usage

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

Arguments

x

Object of class causalmixgpd_ate.

y

Ignored.

type

Character; plot type:

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

  • "effect": ATE curve/points with pointwise CI error bars

  • "arms": treated vs control mean with pointwise CI error bars

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 panel visualizes the posterior summary of the treatment contrast on the mean scale, namely \(E(Y^1) - E(Y^0)\) or its conditional or treated-standardized analogue. The arms panel instead shows the treated and control mean predictions whose difference defines that contrast.

For cate() objects, the x-axis follows the prediction profiles; otherwise it uses the estimated propensity score when available or a simple index order. This keeps the comparison aligned with how the effect object was standardized.

See also

ate, cate, summary.causalmixgpd_ate.

Examples

if (FALSE) { # \dontrun{
ate_result <- cate(fit, newdata = X_new, interval = "credible")
plot(ate_result)  # CATE default: effect plot
plot(ate_result, type = "effect")  # single ATE plot
plot(ate_result, type = "arms")    # single arms plot
} # }