conditionalEffects {cito}

Calculate average conditional effects

Description

Average conditional effects calculate the local derivatives for each observation for each feature. They are similar to marginal effects. And the average of these conditional effects is an approximation of linear effects (see Pichler and Hartig, 2023 for more details). You can use this function to either calculate main effects (on the diagonal, take a look at the example) or interaction effects (off-diagonals) between features.

To obtain uncertainties for these effects, enable the bootstrapping option in the dnn(..) function (see example).

Usage

conditionalEffects(
  object,
  interactions = FALSE,
  epsilon = 0.1,
  device = c("cpu", "cuda", "mps"),
  indices = NULL,
  data = NULL,
  type = "response",
  ...
)

## S3 method for class 'citodnn'
conditionalEffects(
  object,
  interactions = FALSE,
  epsilon = 0.1,
  device = c("cpu", "cuda", "mps"),
  indices = NULL,
  data = NULL,
  type = "response",
  ...
)

## S3 method for class 'citodnnBootstrap'
conditionalEffects(
  object,
  interactions = FALSE,
  epsilon = 0.1,
  device = c("cpu", "cuda", "mps"),
  indices = NULL,
  data = NULL,
  type = "response",
  ...
)

Arguments

object

object of class citodnn

interactions

calculate interactions or not (computationally expensive)

epsilon

difference used to calculate derivatives

device

which device

indices

of variables for which the ACE are calculated

data

data which is used to calculate the ACE

type

ACE on which scale (response or link)

...

additional arguments that are passed to the predict function

Value

an S3 object of class "conditionalEffects" is returned. The list consists of the following attributes:

result

3-dimensional array with the raw results

mean

Matrix, average conditional effects

abs

Matrix, summed absolute conditional effects

sd

Matrix, standard deviation of the conditional effects

Author(s)

Maximilian Pichler

References

Scholbeck, C. A., Casalicchio, G., Molnar, C., Bischl, B., & Heumann, C. (2022). Marginal effects for non-linear prediction functions. arXiv preprint arXiv:2201.08837.

Pichler, M., & Hartig, F. (2023). Can predictive models be used for causal inference?. arXiv preprint arXiv:2306.10551.

Examples

if(torch::torch_is_installed()){
library(cito)

# Build and train  Network
nn.fit = dnn(Sepal.Length~., data = datasets::iris)

# Calculate average conditional effects
ACE = conditionalEffects(nn.fit)

## Main effects (categorical features are not supported)
ACE

## With interaction effects:
ACE = conditionalEffects(nn.fit, interactions = TRUE)
## The off diagonal elements are the interaction effects
ACE[[1]]$mean
## ACE is a list, elements correspond to the number of response classes
## Sepal.length == 1 Response so we have only one
## list element in the ACE object

# Re-train NN with bootstrapping to obtain standard errors
nn.fit = dnn(Sepal.Length~., data = datasets::iris, bootstrap = 30L)
## The summary method calculates also the conditional effects, and if
## bootstrapping was used, it will also report standard errors and p-values:
summary(nn.fit)


}