Accompanying code for the paper "A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment".

Last update: Nov 25, 2021

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#backdoor-HSIC (bd_HSIC)

Accompanying code for the paper "A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment".

To generate the synthetic data used in the paper, the general procedure is the following:

Generate the necessary data
Generate and run the appropriate job using the "interpreter_run.py"
Post-process the results
Generate plots from the results

We list which scripts to run and which job to run to recreate the experiments in the paper.

Experiment	File to generate data	File to run experiments	Post process file	Generate plot
Binary	generate_simple_null.py	do_null_binary	kchsic_categorical_power.py	kchsic_categorical_power.py
Linear continuous	generate_data_multivariate.py	kc_rule_new	post_process.py	create_plots.py
Mixed	generate_mixed_data.py	do_null_mix	post_process.py	create_plots.py
Non-linear continuous	generate_hdm_breaker.py	hdm_breaker_fam_y=i_100, i=1,4	post_process.py	create_plots.py
Linear kernel binary	generate_simple_null.py	do_null_binary_linear_kernel	kchsic_categorical_power.py	kchsic_categorical_power.py
Linear kernel continuous	generate_data_multivariate.py	linear	post_process.py	create_plots.py

To get the results for the benchmark post-double-selection method and the R scripts can be run. To run the R scripts, some .pt files need to be converted to .csv. To do this, use the pt_to_csv.py script. For the real world experiments, do the following:

Lalonde - Run lalonde_real_experiment.py
Twins - Process the data using twins_preprocess.py, then run twins_real_experiment.py

This repository will include the results needed to recreate the plots in the paper already.

Accompanying code for the paper "A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment".

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Robert Hu

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