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

Code for approximate graph reduction techniques for cardinality-based DSFM, from paper

Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling

Pytorch and Torch testing code of CartoonGAN

An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

I-BERT: Integer-only BERT Quantization

Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond

Realtime_Multi-Person_Pose_Estimation

Redash reset for python

BERTMap: A BERT-Based Ontology Alignment System

This repository contains several jupyter notebooks to help users learn to use neon, our deep learning framework

Listing arxiv - Personalized list of today's articles from ArXiv

HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events globally on daily to subseasonal timescales.

🔥3D-RecGAN in Tensorflow (ICCV Workshops 2017)

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predicate.

Unofficial PyTorch Implementation for HifiFace (https://arxiv.org/abs/2106.09965)

EdiBERT, a generative model for image editing

An end-to-end framework for mixed-integer optimization with data-driven learned constraints.

chainladder - Property and Casualty Loss Reserving in Python