jaabmar

acp

Code for the paper "Approximating full conformal prediction at scale via influence functions""

arXausality

A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.

Awesome-explainable-AI

A collection of research materials on explainable AI/ML

causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning

causalml

Uplift modeling and causal inference with machine learning algorithms

confounder-lower-bound

Code for the paper "Hidden yet quantifiable: A lower bound for confounding strength using randomized trials"

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

DUN

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

kernel-test-bias

Implementation for the paper: "Detecting critical treatment effect bias in small subgroups"

private-pgd

Implementation for the paper: "Privacy-preserving data release leveraging optimal transport and particle gradient descent"

jaabmar/confounder-lower-bound

Code for the paper "Hidden yet quantifiable: A lower bound for confounding strength using randomized trials"

jaabmar/kernel-test-bias

Implementation for the paper: "Detecting critical treatment effect bias in small subgroups"

jaabmar/private-pgd

Implementation for the paper: "Privacy-preserving data release leveraging optimal transport and particle gradient descent"

jaabmar/arXausality

A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.

jaabmar/Awesome-explainable-AI

A collection of research materials on explainable AI/ML

jaabmar/causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning

jaabmar/causalml

Uplift modeling and causal inference with machine learning algorithms

jaabmar/dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

jaabmar/DUN

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

jaabmar/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

jaabmar/exact-cp-optimization

Code accompaining the paper "Exact Optimization of Conformal Predictors via Incremental and Decremental Learning"

jaabmar/influence-release

jaabmar/jaabmar

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jaabmar/mlforhealthlabpub

Machine Learning and Artificial Intelligence for Medicine.

jaabmar/Model-Interpretation

Overview of different model interpretability libraries.