Provides a comprehensive set of datasets and tools for 'causal inference' research.
The package includes data from clinical trials, cancer studies, epidemiological surveys, environmental exposures, and health-related observational studies.
Designed to facilitate causal analysis, risk assessment, and advanced statistical modeling,
it leverages datasets from packages such as 'causalOT', 'survival', 'causalPAF', 'evident', 'melt', and 'sanon'.
The package is inspired by the foundational work of Pearl (2009)
The ForCausality package provides a curated and comprehensive collection of datasets designed for causal inference research. It brings together data from diverse domains such as clinical trials, cancer studies, epidemiological surveys, environmental exposures, and health-related observational studies.
You can install the ForCausality package from CRAN with the following R function:
install.packages("ForCausality")
Each dataset in ForCausality is labeled with a sufix to indicate its structure and type:
_df: A standar dataframe.
_tbl_df: A tibble data frame object.
_list: A list object.
Colon_df: Chemotherapy for Stage B/C colon cancer
Stroke_df: Fictional ischemic stroke data case control data with risk factors, exposures and confounders
Pph_df: An external control trial of treatments for post-partum hemorrhage
# Load the package
library(ForCausality)
# Load a dataset from the package
data(Colon_df)
# Show the first six rows of the dataset
head(Colon_df)
# Display the structure of the dataset
str(Colon_df)
# Summarize key variables
summary(Colon_df)
# Visualize treatment groups and survival status
table(Colon_df$treatment, Colon_df$survival)
# Open the dataset in the RStudio viewer
View(Colon_df)