hknd23
Assistant Professor, Hitotsubashi Institute for Advanced Study (HIAS)
Hitotsubashi UniversityKunitachi, Tokyo, Japan
Pinned Repositories
binomialDP
Differentially Private Inference for Binomial Data
DATASET_MA
Files for MA Data
DeepLearningCausal
functions to estimate the Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT)
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.
hknd23.github.io
Nguyen Huynh's Professional Information
idcempy
Inflated Discrete Choice Models
MarginalEffectsPlots
Code for creating marginal effects plots, adapted from https://rpubs.com/milesdwilliams15/381372 by Miles D. Williams
MID-Statement-Files
V3 of Keywords+ MID Statement Files
sf
Simple Features for R
BayesSPsurv
Bayesian Spatial Split Population Survival Model
hknd23's Repositories
hknd23/idcempy
Inflated Discrete Choice Models
hknd23/DeepLearningCausal
functions to estimate the Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT)
hknd23/binomialDP
Differentially Private Inference for Binomial Data
hknd23/DATASET_MA
Files for MA Data
hknd23/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.
hknd23/hknd23.github.io
Nguyen Huynh's Professional Information
hknd23/MarginalEffectsPlots
Code for creating marginal effects plots, adapted from https://rpubs.com/milesdwilliams15/381372 by Miles D. Williams
hknd23/MID-Statement-Files
V3 of Keywords+ MID Statement Files
hknd23/sf
Simple Features for R
hknd23/zmiop
Zero and Middle Inflated Ordered Probit Models