| Type: | Package |
| Title: | A General Causal Inference Framework by Encoding Generative Modeling |
| Version: | 0.3.3 |
| Date: | 2023-03-22 |
| Maintainer: | Qiao Liu <liuqiao@stanford.edu> |
| Description: | CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) <doi:10.48550/arXiv.2212.05925>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Imports: | reticulate |
| URL: | https://github.com/SUwonglab/CausalEGM |
| BugReports: | https://github.com/SUwonglab/CausalEGM/issues |
| Repository: | CRAN |
| Depends: | R(≥ 3.6.0) |
| RoxygenNote: | 7.2.3 |
| Suggests: | rmarkdown, knitr, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2023-03-28 01:42:24 UTC; kimmo |
| Author: | Qiao Liu [aut, cre], Wing Wong [aut], Balasubramanian Narasimhan [ctb] |
| Date/Publication: | 2023-03-28 12:00:11 UTC |
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 |
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 |
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 |
h_units |
is the list of hidden nodes in the h network for predicting |
dv_units |
is the list of hidden nodes in the discriminator for distribution match |
dz_units |
is the list of hidden nodes in the discriminator for distribution match |
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 |
use_v_gan |
whether to use the GAN training for |
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))
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 |
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