Package 'RcausalEGM'

Main function for estimating causal effect in either binary or continuous treatment settings.

Description

This function takes observation data (x,y,v) as input, and estimate the ATE/ITE/ADRF.

Usage

causalegm(
  x,
  y,
  v,
  z_dims = c(3, 3, 6, 6),
  output_dir = ".",
  dataset = "myData",
  lr = 2e-04,
  bs = 32,
  alpha = 1,
  beta = 1,
  gamma = 10,
  g_d_freq = 5,
  g_units = c(64, 64, 64, 64, 64),
  e_units = c(64, 64, 64, 64, 64),
  f_units = c(64, 32, 8),
  h_units = c(64, 32, 8),
  dv_units = c(64, 32, 8),
  dz_units = c(64, 32, 8),
  save_model = FALSE,
  save_res = FALSE,
  binary_treatment = TRUE,
  use_z_rec = TRUE,
  use_v_gan = TRUE,
  random_seed = 123,
  n_iter = 30000,
  normalize = FALSE,
  x_min = NULL,
  x_max = NULL
)

Arguments

x	is the treatment variable, one-dimensional array with size n.
y	is the potential outcome, one-dimensional array with size n.
v	is the covariates, two-dimensional array with size n by p.
z_dims	is the latent dimensions for $z_0,z_1,z_2,z_3$ respectively. Total dimension should be much smaller than the dimension of covariates $v$. Default: c(3,3,6,6)
output_dir	is the folder to save the results including model hyperparameters and the estimated causal effect. Default is ".".
dataset	is the name for the input data. Default: "myData".
lr	is the learning rate. Default: 0.0002.
bs	is the batch size. Default: 32.
alpha	is the coefficient for the reconstruction loss. Default: 1.
beta	is the coefficient for the MSE loss of $x$ and $y$. Default: 1.
gamma	is the coefficient for the gradient penalty loss. Default: 10.
g_d_freq	is the iteration frequency between training generator and discriminator in the Roundtrip framework. Default: 5.
g_units	is the list of hidden nodes in the generator/decoder network. Default: c(64,64,64,64,64).
e_units	is the list of hidden nodes in the encoder network. Default: c(64,64,64,64,64).
f_units	is the list of hidden nodes in the f network for predicting $y$. Default: c(64,32,8).
h_units	is the list of hidden nodes in the h network for predicting $x$. Default: c(64,32,8).
dv_units	is the list of hidden nodes in the discriminator for distribution match $v$. Default: c(64,32,8).
dz_units	is the list of hidden nodes in the discriminator for distribution match $z$. Default: c(64,32,8).
save_model	whether to save the trained model. Default: FALSE.
save_res	whether to save the results during training. Default: FALSE.
binary_treatment	whether the treatment is binary or continuous. Default: TRUE.
use_z_rec	whether to use the reconstruction loss for $z$. Default: TRUE.
use_v_gan	whether to use the GAN training for $v$. Default: TRUE.
random_seed	is the random seed to fix randomness. Default: 123.
n_iter	is the training iterations. Default: 30000.
normalize	whether apply normalization to covariates. Default: FALSE.
x_min	ADRF start value. Default: NULL
x_max	ADRF end value. Default: NULL

Value

causalegm returns an object of class "causalegm".

An object of class "causalegm" is a list containing the following:

causal_pre	the predicted causal effects, which are individual causal effects (ITEs) in binary treatment settings and dose-response values in continous treatment settings.
getCATE	the method for getting the conditional average treatment effect (CATE).It takes covariates v as input.
predict	the method for outcome function. It takes treatment x and covariates v as inputs.

References

Qiao Liu, Zhongren Chen, Wing Hung Wong. CausalEGM: a general causal inference framework by encoding generative modeling. arXiv preprint arXiv:2212.05925, 2022.

Examples

#Generate a simple simulation data.
n <- 1000
p <- 10
v <- matrix(rnorm(n * p), n, p)
x <- rbinom(n, 1, 0.4 + 0.2 * (v[, 1] > 0))
y <- pmax(v[, 1], 0) * x + v[, 2] + pmin(v[, 3], 0) + rnorm(n)
model <- causalegm(x=x, y=y, v=v, n_iter=3000)
paste("The average treatment effect (ATE):", round(model$ATE, 2))

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Make predictions with causalEGM model.

Description

When x is NULL, the conditional average treatment effect (CATE), namely tau(v), is estimated using a trained causalEGM model. When x is provided, estimating the potential outcome y given treatment x and covariates v using a trained causalEGM model.

Usage

get_est(object, v, x = NULL)

Arguments

object	An object of class "causalegm".
v	is the covariates, two-dimensional array with size n by p.
x	is the optional treatment variable, one-dimensional array with size n. Defaults to NULL.

Value

Vector of predictions.

Examples

#Generate a simple simulation data.
n <- 1000
p <- 10
v <- matrix(rnorm(n * p), n, p)
x <- rbinom(n, 1, 0.4 + 0.2 * (v[, 1] > 0))
y <- pmax(v[, 1], 0) * x + v[, 2] + pmin(v[, 3], 0) + rnorm(n)
model <- causalegm(x=x, y=y, v=v, n_iter=3000)
n_test <- 100
v_test <- matrix(rnorm(n_test * p), n_test, p)
x_test <- rbinom(n_test, 1, 0.4 + 0.2 * (v_test[, 1] > 0))
pred_cate <- get_est(model, v = v_test) # CATE estimate
pred_y <- get_est(model, v = v_test, x = x_test) # y given treatment x plus covariates v

---

Install the python CausalEGM package

Description

Install the python CausalEGM package

Usage

install_causalegm(method = "auto", pip = TRUE)

Arguments

method	default "auto"
pip	boolean flag, default TRUE

Value

No return value

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