source code for 'Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge' by A. Shah, K. Shanmugam, K. Ahuja

Overview

Source code for "Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge"

Reference: Abhin Shah, Karthikeyan Shanmugam, Kartik Ahuja, "Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge," The 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Contact: [email protected]

Arxiv: https://arxiv.org/pdf/2106.11560.pdf

Dependencies:

In order to successfully execute the code, the following libraries must be installed:

  1. Python --- causallib, sklearn, multiprocessing, contextlib, scipy, functools, pandas, numpy, itertools, random, argparse, time, matplotlib, pickle, pyreadr, rpy2, torch

  2. R --- RCIT

Command inputs:

  • nr: number of repetitions (default = 100)
  • no: number of observations (default = 50000)
  • use_t_in_e: indicator for whether t should be used to generate e (default = 1)
  • ne: number of environments (default = 3)
  • number_IRM_iterations - number of iterations of IRM (default = 15000)
  • nrd - number of features for sparse subset search (default = 5)

Reproducing the figures and tables:

  1. To reproduce Figure 3a and Figure 10a, run the following three commands:
$ mkdir synthetic_theory
$ python3 -W ignore synthetic_theory.py --nr 100
$ python3 plot_synthetic_theory.py --nr 100
  1. To reproduce Figure 3b and Figure 10b, run the following three commands:
$ mkdir synthetic_algorithms
$ python3 -W ignore synthetic_algorithms.py --nr 100
$ python3 plot_synthetic_algorithms.py --nr 100
  1. To reproduce Figure 3c, run the following three commands:
$ mkdir synthetic_high_dimension
$ python3 -W ignore synthetic_high_dimension.py --nr 100
$ python3 plot_synthetic_high_dimension.py --nr 100
  1. To reproduce Table 1, run the following two commands:
$ mkdir syn-entner 
$ python3 -W ignore syn-entner --nr 100
  1. To reproduce Table 2, run the following two commands:
$ mkdir syn-cheng 
$ python3 -W ignore syn-cheng --nr 100
  1. To reproduce Figure 4, Figure 12a and Figure 12b, run the following three commands:
$ mkdir ihdp
$ python3 -W ignore ihdp.py --nr 100
$ python3 plot_ihdp.py --nr 100
  1. To reproduce Figure 5, run the following three commands:
$ mkdir cattaneo
$ python3 -W ignore cattaneo.py --nr 100
$ python3 plot_cattaneo.py --nr 100
  1. To reproduce Figure 11a and Figure 11c, run the following three commands:
$ mkdir synthetic_theory
$ python3 -W ignore synthetic_theory.py --nr 100 --use_t_in_e 0
$ python3 plot_synthetic_theory.py --nr 100 --use_t_in_e 0
  1. To reproduce Figure 11b and Figure 11d, run the following three commands:
$ mkdir synthetic_algorithms
$ python3 -W ignore synthetic_algorithms.py --nr 100 --use_t_in_e 0
$ python3 plot_ synthetic_algorithms.py --nr 100 --use_t_in_e 0
Owner
Abhin Shah
Graduate student at MIT. Former undergrad at IITBombay. Former intern at IBM and EPFL
Abhin Shah
A collection of IPython notebooks covering various topics.

ipython-notebooks This repo contains various IPython notebooks I've created to experiment with libraries and work through exercises, and explore subje

John Wittenauer 2.6k Jan 01, 2023
Freecodecamp Scientific Computing with Python Certification; Solution for Challenge 2: Time Calculator

Assignment Write a function named add_time that takes in two required parameters and one optional parameter: a start time in the 12-hour clock format

Hellen Namulinda 0 Feb 26, 2022
Single-Shot Motion Completion with Transformer

Single-Shot Motion Completion with Transformer 👉 [Preprint] 👈 Abstract Motion completion is a challenging and long-discussed problem, which is of gr

FuxiCV 78 Dec 29, 2022
Codes for "Template-free Prompt Tuning for Few-shot NER".

EntLM The source codes for EntLM. Dependencies: Cuda 10.1, python 3.6.5 To install the required packages by following commands: $ pip3 install -r requ

77 Dec 27, 2022
GraphLily: A Graph Linear Algebra Overlay on HBM-Equipped FPGAs

GraphLily: A Graph Linear Algebra Overlay on HBM-Equipped FPGAs GraphLily is the first FPGA overlay for graph processing. GraphLily supports a rich se

Cornell Zhang Research Group 39 Dec 13, 2022
Libtorch yolov3 deepsort

Overview It is for my undergrad thesis in Tsinghua University. There are four modules in the project: Detection: YOLOv3 Tracking: SORT and DeepSORT Pr

Xu Wei 226 Dec 13, 2022
🔥 Cannlytics-powered artificial intelligence 🤖

Cannlytics AI 🔥 Cannlytics-powered artificial intelligence 🤖 🏗️ Installation 🏃‍♀️ Quickstart 🧱 Development 🦾 Automation 💸 Support 🏛️ License ?

Cannlytics 3 Nov 11, 2022
1st ranked 'driver careless behavior detection' for AI Online Competition 2021, hosted by MSIT Korea.

2021AICompetition-03 본 repo 는 mAy-I Inc. 팀으로 참가한 2021 인공지능 온라인 경진대회 중 [이미지] 운전 사고 예방을 위한 운전자 부주의 행동 검출 모델] 태스크 수행을 위한 레포지토리입니다. mAy-I 는 과학기술정보통신부가 주최하

Junhyuk Park 9 Dec 01, 2022
HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records

HiPAL Code for KDD'22 Applied Data Science Track submission -- HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electro

Hanyang Liu 4 Aug 08, 2022
Pytorch Lightning code guideline for conferences

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

Pytorch Lightning 1k Jan 06, 2023
ByteTrack超详细教程!训练自己的数据集&&摄像头实时检测跟踪

ByteTrack超详细教程!训练自己的数据集&&摄像头实时检测跟踪

Double-zh 45 Dec 19, 2022
TyXe: Pyro-based BNNs for Pytorch users

TyXe: Pyro-based BNNs for Pytorch users TyXe aims to simplify the process of turning Pytorch neural networks into Bayesian neural networks by leveragi

87 Jan 03, 2023
Automated Attendance Project Using Face Recognition

dependencies for project: cmake 3.22.1 dlib 19.22.1 face-recognition 1.3.0 openc

Rohail Taha 1 Jan 09, 2022
source code of Adversarial Feedback Loop Paper

Adversarial Feedback Loop [ArXiv] [project page] Official repository of Adversarial Feedback Loop paper Firas Shama, Roey Mechrez, Alon Shoshan, Lihi

17 Jul 20, 2022
Get started learning C# with C# notebooks powered by .NET Interactive and VS Code.

.NET Interactive Notebooks for C# Welcome to the home of .NET interactive notebooks for C#! How to Install Download the .NET Coding Pack for VS Code f

.NET Platform 425 Dec 25, 2022
Repository for Multimodal AutoML Benchmark

Benchmarking Multimodal AutoML for Tabular Data with Text Fields Repository for the NeurIPS 2021 Dataset Track Submission "Benchmarking Multimodal Aut

Xingjian Shi 44 Nov 24, 2022
ICSS - Interactive Continual Semantic Segmentation

Presentation This repository contains the code of our paper: Weakly-supervised c

Alteia 9 Jul 23, 2022
[IEEE Transactions on Computational Imaging] Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting

Few-shot Deep HDR Deghosting This repository contains code and pretrained models for our paper: Self-Gated Memory Recurrent Network for Efficient Scal

Susmit Agrawal 4 Dec 29, 2021
Registration Loss Learning for Deep Probabilistic Point Set Registration

RLLReg This repository contains a Pytorch implementation of the point set registration method RLLReg. Details about the method can be found in the 3DV

Felix Järemo Lawin 35 Nov 02, 2022
QI-Q RoboMaster2022 CV Algorithm

QI-Q RoboMaster2022 CV Algorithm

2 Jan 10, 2022