QReR

The running codes for "Quasi-Rerandomization for Observational Studies".

Notebooks (.ipynb):

• Generate_SimuDatasets_3Scenario: Generate simulated datasets for comparisons.
• QReR_Simulation_ReR-SATE: Simulated studies to compare QReR and ReR for $\tau_{\rm SATE}$ estimation. (Tables 1-3)
• Benchmark_Simulation-PATE: Simulated studies to compare different benchmark methods for $\tau_{\rm PATE}$ estimation. (Table 4)
• QReR_Simulation-PATE: Simulated studies to evaluate QReR for estimating $\tau_{\rm PATE}$. (Table 4)
• QReR_RealData_IHDP_SATE&PATE(100): Perform real data analysis over IHDP datasets. (Figure 2 & Tables 5-6)
• QReR_Demo: Visually balance covariates based on QReR and ReR. (Figure 1)

Scripts (.py):

• benchmarks: Wrappers to implement IPW,FM,EBAL,SBW and EBCW.
• datagen: Functions to generate simulated datasets.
• network: The network structure of QReR.

Real Datasets (IHDP):

• The real datasets (IHDP-100 (train), IHDP-100 (test)) can be found in folder realdata, which are available at https://www.fredjo.com/.
• The data descriptions can be found at Section 5.1 of the paper "Estimating individual treatment effect: generalization bounds and algorithms".