Semi-Supervised Estimation of Average Treatment Effects

Provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) . A detailed vignette is included.

SMMAL

SMMAL is an R package for estimating the Average Treatment Effect (ATE) using semi-supervised learning (SSL), tailored for settings with limited treatment/outcome labels but rich covariates and surrogate variables. It enhances efficiency and robustness over supervised methods by leveraging unlabeled data and supports high-dimensional models via cross-fitting, flexible model fitting, and adaptive LASSO.

Installation

# install.packages("devtools")
devtools::install_github("ShuhengKong/SMMAL")

A github version can be found at this link: https://github.com/ShuhengKong/SMMAL

Example

This is a basic example which shows you how to solve a common problem:

library(SMMAL)

# Load the example dataset included with the package
file_path <- system.file("extdata", "sample_data.rds", package = "SMMAL")
dat <- readRDS(file_path)

temp <- data.frame(dat$X)
temp[,] <- NA
# Estimate ATE using the SMMAL pipeline
output <- SMMAL(
  Y = dat$Y,
  A = dat$A,
  S = data.frame(dat$S),
  X = data.frame(dat$X),
  nfold = 5,
  cf_model = "bspline"
)

# View the results
print(output)
#> $est
#> [1] 0.1021349
#> 
#> $se
#> [1] 0.03006258

SMMAL input files

SMMAL output