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

The pyrelational package offers a flexible workflow to enable active learning with as little change to the models and datasets as possible

python library for invisible image watermark (blind image watermark)

GLANet - The code for Global and Local Alignment Networks for Unpaired Image-to-Image Translation arxiv

A programming language written with python

Zeyuan Chen, Yangchao Wang, Yang Yang and Dong Liu.

Text-Based Ideal Points

Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

EfficientNetV2 implementation using PyTorch

Repository for XLM-T, a framework for evaluating multilingual language models on Twitter data

pytorch implementation of dftd2 & dftd3

This repo contains the official code and pre-trained models for the Dynamic Vision Transformer (DVT).

The project covers common metrics for super-resolution performance evaluation.

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

A simple log parser and summariser for IIS web server logs

Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.

Official Implementation of SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'

Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)

One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing".