Javier Abad's Projects
A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.
A collection of research materials on explainable AI/ML
Must-read papers and resources related to causal inference and machine (deep) learning
Uplift modeling and causal inference with machine learning algorithms
Code for the paper "Hidden yet quantifiable: A lower bound for confounding strength using randomized trials"
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.
Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
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.
Code accompaining the paper "Exact Optimization of Conformal Predictors via Incremental and Decremental Learning"
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Implementation for the paper: "Detecting critical treatment effect bias in small subgroups"
Machine Learning and Artificial Intelligence for Medicine.
Overview of different model interpretability libraries.
Implementation for the paper: "Privacy-preserving data release leveraging optimal transport and particle gradient descent"