Replication Files for Park and Kang (2019)

This Github repository contains replication files for Park and Kang (2019).

Folders

ECLSK

ECLSK folder contains 1.Analysis.R, and 3.Summary.R files, and replicates the data analysis in Section 6 of the paper.

• 1.Analysis.R file cleans the raw data (available at https://nces.ed.gov/ecls/dataproducts.asp), implements our method, and generates Result_BATCH####.csv files in NData folder.

• 2.Summary.R file summarizes Result_BATCH####.csv files and estimates the groupwise effects based on the causal forest using grf package.

• NData folder contains csv files titled as BATCH_BATCH####.csv. Here B#### represents the index of sample splitting procedure ranging from B0001 to B0100.

Simulation

Simulation folder contains plot, and Result, Summary_Result folders, and 1.Estimation.R and 2.Summary.R files.

• 1.Estimation.R replicates the simulation analysis in Section 4 of the paper. The result of the simulation is saved in Result folder.

• 2.Summary.R summarizes the results in Result folder.

• Result folder contains csv files titled as Result_###_COR_###_rho###_B####_SubB####.csv. The first ### takes either CPS or VPS according to the propensity score model. The second ### takes either Est or CL according to the i.i.d. model or the clustered model. The third #### varies from 0000 to 0003 according to the effect heterogeneity. The last #### varies from 0000 to 0100 according to the index of sample splitting procedure.

• Summary_Result folder contains csv files titled as Result_###_COR_###_rho###_B####.csv which is the merged file of the csv files in Result folder with the format Result_###_COR_###_rho###_B####_SubB####.csv.

• plot folder contains graphical summary of the simulation results.

Code

• MySL.R contains functions used for implementing superlearner algorithm and estimating the nuisance functions.

• SSLS.R contains functions used for the estimation of the groupwise effect using the methods in Section 2 and 3 of the main paper.

References

Chan Park & Hyunseung Kang (2019) A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference, arXiv:1908.04427 [link]