Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

Last update: Jun 23, 2022

Related tags

Overview

🍐 quince

Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

🍐 Installation

$ git clone [email protected]:anndvision/quince.git
$ cd quince
$ conda env create -f environment.yml
$ conda activate quince

🍐 Example: Replicating IHDP results

Step 1: Hyperparameter Tuning (optional)

Find the best hyperparameters using the tune function, on a dataset like ihdp for an ensemble model.

$ quince \
    tune \
        --job-dir ~/experiments/quince/tuning/ \
        --max-samples 500 \
        --gpu-per-trial 0.2 \
    ihdp \
    ensemble

Step 2: Train ensembles over a number of trials

Here, we use the train function to fit an ensemble of mixture density networks on 10 realizations of the ihdp with hidden confounding dataset. For the full results change --num-trials 1000

$ quince \
    train \
        --job-dir ~/experiments/quince/ \
        --num-trials 10 \
        --gpu-per-trial 0.2 \
    ihdp \
    ensemble \
        --dim-hidden 200 \
        --num-components 5 \
        --depth 4 \
        --negative-slope 0.3 \
        --dropout-rate 0.5 \
        --spectral-norm 6.0 \
        --learning-rate 5e-4 \
        --batch-size 200 \
        --epochs 500 \
        --ensemble-size 10

Step 3: Evaluate

Plots will be written to the experiment-dir

$ quince \
    evaluate \
        --experiment-dir ~/experiments/quince/ihdp/hc-True_beta-None/ensemble/dh-200_nc-5_dp-4_ns-0.3_dr-0.5_sn-6.0_lr-0.0005_bs-200_ep-500/ \
    compute-intervals \
        --gpu-per-trial 0.2 \
    compute-intervals-kernel \
        --gpu-per-trial 0.2 \
    plot-deferral \
    plot-errorbars \
        --trial 0

🍐 Replicating Other Results

Simulated Data

$ quince \
    train \
        --job-dir ~/experiments/quince/ \
        --num-trials 50 \
        --gpu-per-trial 0.2 \
    synthetic \
        --gamma-star 1.65 \
    ensemble \
        --dim-hidden 200 \
        --num-components 5 \
        --depth 4 \
        --negative-slope 0.0 \
        --dropout-rate 0.1 \
        --spectral-norm 6.0 \
        --learning-rate 1e-3 \
        --batch-size 32 \
        --epochs 500 \
        --ensemble-size 10

$ quince \
    evaluate \
        --experiment-dir ~/experiments/quince/synthetic/ne-1000_gs-1.65_th-4.00_be-0.75_si-1.00_dl-2.00/ensemble/dh-200_nc-5_dp-4_ns-0.0_dr-0.1_sn-6.0_lr-0.001_bs-32_ep-500/ \
    compute-intervals \
        --gpu-per-trial 0.2 \
    compute-intervals-kernel \
        --gpu-per-trial 0.2 \
    plot-ignorance \
    print-summary \
    print-summary-kernel \
    paired-t-test

Repeat the above for --gamma-star 2.72 and --gamma-star 4.48.

HCMNIST

$ quince \
    train \
        --job-dir ~/experiments/quince/ \
        --num-trials 20 \
        --gpu-per-trial 0.5 \
    hcmnist \
        --gamma-star 1.65 \
    ensemble \
        --dim-hidden 200 \
        --num-components 5 \
        --depth 2 \
        --negative-slope 0.0 \
        --dropout-rate 0.15 \
        --spectral-norm 3.0 \
        --learning-rate 5e-4 \
        --batch-size 200 \
        --epochs 500 \
        --ensemble-size 5

$ quince \
    evaluate \
        --experiment-dir ~/experiments/quince/hcmnist/gs-1.65_th-4.00_be-0.75_si-1.00_dl-2.00/ensemble/dh-200_nc-5_dp-2_ns-0.0_dr-0.15_sn-3.0_lr-0.0005_bs-200_ep-500/ \
    compute-intervals \
        --gpu-per-trial 1.0 \
    print-summary

Repeat the above for --gamma-star 2.72 and --gamma-star 4.48.

Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

Related tags

Overview

🍐 quince

🍐 Installation

🍐 Example: Replicating IHDP results

Step 1: Hyperparameter Tuning (optional)

Step 2: Train ensembles over a number of trials

Step 3: Evaluate

🍐 Replicating Other Results

Simulated Data

HCMNIST

Owner

Andrew Jesson

A minimalist tool to display a network graph.

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR 2022)

Single Image Random Dot Stereogram for Tensorflow

i-RevNet Pytorch Code

Pytorch code for our paper Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains)

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Optimizes image files by converting them to webp while also updating all references.

An efficient and easy-to-use deep learning model compression framework

nfelo: a power ranking, prediction, and betting model for the NFL

Official Implementation and Dataset of "PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency", CVPR 2021

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)

Source code of article "Towards Toxic and Narcotic Medication Detection with Rotated Object Detector"

Finetune SSL models for MOS prediction

Simple ray intersection library similar to coldet - succedeed by libacc

This is code of book "Learn Deep Learning with PyTorch"

Image De-raining Using a Conditional Generative Adversarial Network

Existing Literature about Machine Unlearning

Codes for building and training the neural network model described in Domain-informed neural networks for interaction localization within astroparticle experiments.

Mscp jamf - Build compliance in jamf

PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"