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

Last update: Nov 25, 2021

Related tags

Deep Learning kgformula

Overview

#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".

Related tags

Overview

Owner

Robert Hu

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

TorchMD-Net provides state-of-the-art graph neural networks and equivariant transformer neural networks potentials for learning molecular potentials

A PyTorch implementation of Radio Transformer Networks from the paper "An Introduction to Deep Learning for the Physical Layer".

Repository for the AugmentedPCA Python package.

Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch

Source code for the paper "PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction" in ACL2021

PROJECT - Az Residential Real Estate Analysis

Minimalist Error collection Service compatible with Rollbar clients. Sentry or Rollbar alternative.

Development kit for MIT Scene Parsing Benchmark

Code & Data for Enhancing Photorealism Enhancement

Prml - Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

Trustworthy AI related projects

Face recognition system using MTCNN, FACENET, SVM and FAST API to track participants of Big Brother Brasil in real time.

Captcha-tensorflow - Image Captcha Solving Using TensorFlow and CNN Model. Accuracy 90%+

The Official PyTorch Implementation of "LSGM: Score-based Generative Modeling in Latent Space" (NeurIPS 2021)

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

A simple image/video to Desmos graph converter run locally

Face uncertainty quantification or estimation using PyTorch.

MediaPipe is a an open-source framework from Google for building multimodal

[CVPR 2021] Teachers Do More Than Teach: Compressing Image-to-Image Models (CAT)