Replication Files for Park and Kang (2021)

This Github repository contains replication files for Park and Kang (2021).

Files

ClutserFtSource.R and MySL.R contain functions that are used in the simulation and the data analysis.

Folders

Simulation

Simulation folder replicates Table 5.1 and 5.2 in the paper and here are some details of files and folders.

• 1.GenData.R generates the simulation data sets which are saved in GenData folder. The data sets are titled as Data_N[A]_[B]PS_B[C].csv where [A] indicates the number of cluster ([A]=25,50,100,250,500), [B] indicates the propensity model ([B]=No,Strong), and [C] indicates the simulation replicates ([C]=1,...,210).

• 2.OurMethod.R implements our method and summarizes the result in Ours folder. The result files are titled as N[A]_[B]PS_G[C].csv. [A] indicates the number of cluster ([A]=25,50,100,250,500), [B] indicates the propensity model ([B]=No,Strong), and [C] indicates the magnitude of intra-cluster correlation ([C]=1 and [C]=2 indicates and ).

  • Parallel computing is strongly recommended.

• 3.OtherMethod.R implements competing methods and summarizes the result in Others folder. The result files are titled as Others_N0500_sV_[A]_B[B].csv where [A] indicates the propensity model ([A]=No,Strong) and [B] indicates the simulation replicates ([B]=1,...,210).

  • Parallel computing is strongly recommended.

• 4.Summary.R summarizes the results in Ours and Others folders.

Simulation_ICC

Simulation_ICC replicates Figure A.1 in the supplementary material. 1.Illustration.R generates Illustration_Grid.csv summarizing the simulation results and draws Figure A.1.

Simulation_NonNormal

Simulation_NonNormal replicates Table 5.3 in the paper and here are some details of files and folders.

• 1.GenData.R generates the simulation data sets which are saved in GenData folder. The data sets are titled as Data_N500_[A]PS_B[B].csv where [A] indicates the propensity model ([A]=No,Strong) and [B] indicates the simulation replicates ([C]=1,...,200).

• 2.OurMethod.R implements our method and summarizes the result in Ours folder. The result files are titled as N0500_[A]PS_G1.csv where [A] indicates the propensity model ([A]=No,Strong).

  • Parallel computing is strongly recommended.

• 3.OtherMethod.R implements competing methods and summarizes the result in Others folder. The result files are titled as Others_N0500_sV_[A]_B[B].csv where [A] indicates the propensity model ([A]=No,Strong) and [B] indicates the simulation replicates ([B]=1,...,200).

  • Parallel computing is strongly recommended.

• 4.Summary.R summarizes the results in Ours and Others folders.

Data_ECLSK

Data_ECLSK replicates Section 6.1 and A.3 of the paper and here are some details of files and folders.

• Download data from https://nces.ed.gov/ecls/dataproducts.asp and make ECLSK_Kto8_child_STATA.dta.

• By running 1.ECLSK.R, the data analysis in Section 6.1 of the paper is replicated. In particular,

  • 1.ECLSK.R cleans the raw data and makes Reading1_ECLSK.csv.
  • 1.ECLSK.R implements our method and saves the files in Reading_u folder titled Reading1_[A]_NF[B].csv. Here [A] indicates the nuisance functions and main/auxiliary samples in cross-fitting procedures ([A]=CPS,IPS,OR,SSlist) and [B] indicates the sample split replicates ([B]=1,...,100). The results in Reading_u folder are summarized as Reading_Ours.csv.
  • 1.ECLSK.R implements competing methods and summarizes as Reading_RU.csv and Reading_GRF.csv.
  • Table 6.1, Table A.2, and Figure A.2 are generated.

Data_HIV

Data_HIV replicates Section 6.2 in the paper and here are some details of files and folders.

• Download data from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CVOPZL and use bio_for_analysis.dta.

• By running 1.HIV.R, the data analysis in Section 6.2 of the paper is replicated. In particular,

  • 1.HIV.R cleans the raw data and makes Data_Cleaned.csv.
  • 1.HIV.R implements our method and saves the files in Outcome folder titled Dupas_OR2_NF[A].csv and SSlist_NF[A].csv. Here [A] indicates the sample split replicates ([A]=1,...,100). The results in Outcome folder are summarized as HIV_Ours.csv.
  • 1.HIV.R implements competing methods and summarizes as HIV_RU.csv and HIV_GRF.csv.
  • Table 6.2 is generated.

References

Chan Park & Hyunseung Kang (2021) More Efficient, Doubly Robust, Nonparametric Estimators of Treatment Effects in Multilevel Studies, arXiv:2110.07740 [link]