Description
Bidirectional Two-Stage Least Squares Estimation.
Description
Implements bidirectional two-stage least squares (Bi-TSLS) estimation for identifying bidirectional causal effects between two variables in the presence of unmeasured confounding. The method uses proxy variables (negative control exposure and outcome) along with at least one covariate to handle confounding.
README.md
BiTSLS: Bidirectional Two-Stage Least Squares
A simple R package for estimating bidirectional causal effects using proxy variables.
Installation
# Install from GitHub
# install.packages("devtools")
devtools::install_github("Fhoneysuckle/BiTSLS")
Usage
The Bi_TSLS() function estimates bidirectional causal effects between X and Y:
library(BiTSLS)
# Prepare your data with required variables
data <- data.frame(
X = ..., # Treatment variable
Y = ..., # Outcome variable
Z = ..., # Negative control exposure
W = ..., # Negative control outcome
# Additional covariates (At least one covariate)
)
# Run the estimation
result <- Bi_TSLS(data)
# View results
print(result) # Effect of X on Y and Y on X
Requirements
Your data must contain:
X: Treatment/exposure variable (numeric)Y: Outcome variable (numeric)Z: Negative control exposure (numeric)W: Negative control outcome (numeric)- Additional covariates are optional (At least one covariate)
Sensitivity Analysis
You can test sensitivity to violations of the proxy structural conditions:
# With sensitivity parameters
result <- Bi_TSLS(data, R_w = 0.1, R_z = -0.1)
License
MIT License.