simulated_data_budgetIV: Simulated summary statistics with invalid instruments and...

In budgetIVr: Partial Identification of Causal Effects with Mostly Invalid Instruments

Simulated summary statistics with invalid instruments and nonlinear treatment effect

Description

Example dataset from the nonlinear simulation study using 6 candidate instruments, 3 of which are invalid with violation of IV assumptions (A2) and (A3). See Appx. C.2 of Penn et al. (2025) for technical details or visit the source code for reproducibility, both referenced below. The ground truth causal effect is \Phi^* (X) = (X - 0.25)^2 - 0.25^2. \beta_{\Phi} is taken with respect to the basis functions \Phi = (X, X^2).

Usage

data(simulated_data_budgetIV)

Format

A data frame with 6 rows and 4 columns.

Details

beta_y

Components of the estimator \mathrm{Cov} (Y, Z).

beta_phi_1

Components of the estimator \mathrm{Cov} ( \Phi_1 (X), Z ).

beta_phi_2

Components of the estimator \mathrm{Cov} ( \Phi_2 (X), Z ).

delta_beta_y

Components of the standard error \mathrm{Se} (\mathrm{Cov} (Y, Z)).

Source

The code that generated this dataset was written by the authors and can be found in https://github.com/jpenn2023/budgetIVr/blob/main/paper/simulate_nonlinear_data.R. The dataset is saved as "my_dat R = 0.5 SNR_y = 1.csv".

References

Jordan Penn, Lee Gunderson, Gecia Bravo-Hermsdorff, Ricardo Silva, and David Watson. (2024). BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments. arXiv preprint, 2411.06913.

Examples

data(simulated_data_budgetIV)

beta_y <- simulated_data_budgetIV$beta_y

beta_phi_1 <- simulated_data_budgetIV$beta_phi_1
beta_phi_2 <- simulated_data_budgetIV$beta_phi_2

d_Z <- length(beta_phi_1)

beta_phi <- matrix(c(beta_phi_1, beta_phi_2), nrow = 2, byrow = TRUE)

delta_beta_y <- simulated_data_budgetIV$delta_beta_y

budgetIVr documentation built on April 16, 2025, 5:11 p.m.