Reproducible Research Repository
Reproducible Research Repository
  • Home
  • Repository
  • Collections
  • About
    Home / Repository / PRWP / RR_KEN_2026_585
PRWP

Reproducibility package for SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary

2026
Get Reproducibility Package
Reference ID
RR_KEN_2026_585
Author(s)
Antoine Deeb, Shafkat Farabi, Anja Sautman, Manuel Ramos-Maqueda, Sanmay Das, Wei Lu, Didac Marti Pinto
Collections
World Bank Policy Research Working Papers
Metadata
JSON
Created on
Sep 08, 2026
Last modified
Sep 15, 2026
Page views
6
  • Project Description
  • Downloads
Download related resources
Other Materials
README for the reproducibility package for SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Download [PDF, 314.81 KB]
Download https://reproducibility.worldbank.org//catalog/648/download/1950
Reproducibility verification for SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Download [PDF, 138.81 KB]
Download https://reproducibility.worldbank.org//catalog/648/download/1951
Reproducibility package for SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Download [ZIP, 12.4 MB]
Download https://reproducibility.worldbank.org//catalog/648/download/1952
Zip preview
RR_KEN_2026_585
author_hash_report.csv
data_hash_report.csv
LICENSE.txt
README.pdf
Reproducibility package
02_Hazard_AS.do
analysis_mediator_distribution.ipynb
analyze_and_gen_results.py
clervointLP.py
clervointLPDifferentFormulation.py
clervointQP.py
cluster_analysis.ipynb
code_deviationsimulation_for_each_case.py
config.py
Court_case.py
crps.py
debug.ipynb
double_check.ipynb
global_sd.ipynb
makeplots_reproduction_package.py
make_plots.py
make_plots_with_clustering.ipynb
plotsReproduceRun
Figure 1.png
Figure 5.png
FullDataGroundReproduce
Table 4.png
Table 8.png
FullDataLearningBootstrapReproduce
Table 5 (Initial belief calculated from data columns).png
Table 9 (Initial belief calculated from data columns).png
FullDataLearningFromScratchReproduce
Table 5 (Initial Belief set to N(0,sigma^2)) columns.png
Table 9 (Initial Belief set to N(0,sigma^2)) columns.png
Scenario1
Figure 3 bottom.png
Figure 3 middle.png
Figure 3 top.png
Figure 4 left.png
Table 2_knownVA.png
Table 2_meanVA.png
Table 2_sampleVA.png
Table 6_knownVA.png
Table 6_meanVA.png
Table 6_sampleVA.png
Scenario2
Figure 4 right.png
Table 3_knownVA.png
Table 3_meanVA.png
Table 3_sampleVA.png
Table 7_knownVA.png
Table 7_meanVA.png
Table 7_sampleVA.png
plots_for_paper.ipynb
p_val_histogram_stugg.ipynb
reproduce_exp_one_click_LP_refinedC5.ipynb
reproduce_exp_one_click_QP.ipynb
reproduction_package_one_clic.ipynb
requirenments.txt
run_batch_parallel_array_automated.sh
run_batch_parallel_array_automated_toy_example1.sh
run_batch_parallel_array_automated_toy_example1LP.sh
run_batch_parallel_array_automated_toy_example2.sh
run_batch_parallel_array_automated_toy_example2LP.sh
run_batch_parallel_FullDataGround.sh
run_batch_parallel_FullDataGroundLP.sh
run_batch_parallel_FullDataLearningBootstrapCor.sh
run_batch_parallel_FullDataLearningBootstrapLP.sh
run_batch_parallel_FullDataLearningFromScratch.sh
run_batch_parallel_FullDataLearningFromScratchLP.sh
run_plotting.sh
run_seq.sh
run_seq_FullDataFromScratch.sh
run_seq_FullDataGround.sh
run_seq_FullDataGroundLP.sh
run_seq_FullDataLearningBootstrap.sh
run_seq_FullDataLearningBootstrapLP.sh
run_seq_FullDataLearningFromScratch.sh
run_seq_FullDataLearningFromScratchLP.sh
run_seq_toy_example1.sh
run_seq_toy_example1LP.sh
run_seq_toy_example2.sh
run_seq_toy_example2LP.sh
run_simulation.py
run_simulation_product.py
run_simulation_toy_problem.py
run_simulation_toy_problem_loadLP.py
run_simulation_toy_problem_testing.py
sensitivity_analysis_of_VA_esitmation.ipynb
Simulation.py
simulation_for_each_case_joint_distribution.py
simulation_for_each_case_joint_distribution_old_algorithm.py
simulation_lp_withload.py
Simulation_product.py
simulation_utils.py
slacked_LP_product.py
SlakedLP.py
SlakedLPwithLoad.py
SlakedQPwithLoad.py
small_scale_sim.ipynb
test_cases
Scenario1.py
scenario2.py
timeQPvsLP.ipynb
VA_Antoine.py
reproducibility_report_RR_KEN_2026_585.pdf
WB-IGO-RIDER.txt
Back to Catalog
The World Bank Working for a World Free of Poverty
  • IBRD IDA IFC MIGA ICSID

© The World Bank Group, All Rights Reserved.