Pinned Repositories
book
:books: All programming languages books
causalml
Uplift modeling and causal inference with machine learning algorithms
CLAIRE
COVID-datasets
Include 6 different datasets from different data sources for different research purposes.
DIRECT
DNDC
DomainBed
DomainBed is a suite to test domain generalization algorithms
GEAR
GraphCFE
HyperSCI
jma712's Repositories
jma712/HyperSCI
jma712/GEAR
jma712/GraphCFE
jma712/DNDC
jma712/DIRECT
jma712/DomainBed
DomainBed is a suite to test domain generalization algorithms
jma712/book
:books: All programming languages books
jma712/causalml
Uplift modeling and causal inference with machine learning algorithms
jma712/CLAIRE
jma712/COVID-datasets
Include 6 different datasets from different data sources for different research purposes.
jma712/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.
jma712/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.
jma712/GCN_Crowd
jma712/ITProjectTeamDocs
IT project management team docs.
jma712/jma712.github.io
jma712/Selective-Sampling-for-Buildings