Skip to content

Code for the paper titled 'Falsification using higher order influence functions for double machine learning estimators of causal effects'

License

Notifications You must be signed in to change notification settings

cxy0714/Falsification-using-higher-order-influence-functions

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Falsification using higher order influence functions for double machine learning estimators of causal effects

Introduction

Here we provide the code to reproduce the analysis described in:

Organization

  • create_simulation_parameters_continuous_X.R — simulates training and oracle samples, fits nuisance parameters models in the training sample, and computes inverse Gram matrices for the simulation with continuous X.
  • continuous_X_sim.R — simulates estimation samples, computes estimates of counterfactual means, and computes second order bias estimates for the simulation with continuous X.
  • continuous_X_sim_choose_stopping_k.R — uses the approach described in the supplement to choose k for simulation with continuous X.
  • create_simulation_parameters_binary_X.R — simulates training and oracle samples, fits nuisance parameters models in the training sample, and computes inverse Gram matrices for the simulation with binary X.
  • binary_X_sim.R — simulates estimation samples, computes estimates of counterfactual means, and computes second order bias estimates for the simulation with binary X.
  • compute_bias_all_sims.R — computes the conditional bias, and the Cauchy-Schwarz bias for simulations with binary and continuous X.
  • create_simulation_parameters_CRC_GAN_data.R — simulates training and oracle samples, fits nuisance parameters models in the training sample, and computes inverse Gram matrices for the simulation with artificial data based on the National Cancer Database.
  • CRC_GAN_sim.R — simulates estimation samples, computes estimates of counterfactual means, and computes second order bias estimates for the simulation with artificial data based on the National Cancer Database.
  • create_parameters_CRC_real_data.R — sample splits, fits nuisance parameters models, and computes inverse Gram matrices for analysis of the National Cancer Database.
  • CRC_real_data_analysis.R — computes cross-fit estimates of counterfactual means (and the risk difference), and estimates of the second order bias.
  • CRC_real_data_analysis_choose_stopping_k.R — uses the approach described in the supplement to choose k for analysis of the National Cancer Database.
  • process_outputs.R — R file which reproduces the tables and figures displayed in the main text, summarizing simulation results.
  • process_outputs_supplement.R — R file which reproduces the tables and figures displayed in the supplement, summarizing simulation results.
  • src — Folder containing scripts with functions and dependencies to be called by the above files.
  • params — Folder should be created to which simulation parameters, which are outputs of create_simulation_parameters..., are saved.
  • output — Folder to which simulation outputs are saved.
  • figures — Folder containing figures, which are outputs of process_outputs.R and process_outputs_supplement.R.

Correspondence

If you have any questions, comments, or discover an error, please contact Kerollos Wanis at knwanis@gmail.com.

About

Code for the paper titled 'Falsification using higher order influence functions for double machine learning estimators of causal effects'

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • R 100.0%