yuhsienliu
Data Scientist working on Causal Machine Learning with the Generalized Random Forests.
Ontario, Canada
Pinned Repositories
amortization
Python library for calculating amortizations and generating amortization schedules
awesome-readme
A curated list of awesome READMEs
causalml
Uplift modeling and causal inference with machine learning algorithms
CEVAE
Causal Effect Inference with Deep Latent-Variable Models
dragonnet
ds-wgan
Design of Simulations using WGAN
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.
grf
Generalized Random Forests
KNN_img_recognition
KNN_img_recognition
latex-template-arxiv-preprint
A simple LaTeX template for Technical Reports, arXiv preprints & 2-column Conference papers
yuhsienliu's Repositories
yuhsienliu/amortization
Python library for calculating amortizations and generating amortization schedules
yuhsienliu/awesome-readme
A curated list of awesome READMEs
yuhsienliu/causalml
Uplift modeling and causal inference with machine learning algorithms
yuhsienliu/CEVAE
Causal Effect Inference with Deep Latent-Variable Models
yuhsienliu/dragonnet
yuhsienliu/ds-wgan
Design of Simulations using WGAN
yuhsienliu/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.
yuhsienliu/grf
Generalized Random Forests
yuhsienliu/KNN_img_recognition
KNN_img_recognition
yuhsienliu/latex-template-arxiv-preprint
A simple LaTeX template for Technical Reports, arXiv preprints & 2-column Conference papers
yuhsienliu/matplotlib-cheatsheet
Matplotlib 3.1 cheat sheet.
yuhsienliu/moderncv
A modern curriculum vitae class for LaTeX
yuhsienliu/whynot
A Python sandbox for decision making in dynamics