Zhang Chen's repositories
causal-learn
Python translation (and extension) of the Tetrad java code.
CausalInferenceIntro
Causal Inference for the Brave and True的中文翻译版。全部代码基于Python，适用于计量经济学、量化社会学、策略评估等领域。
CTL
A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python
DiD
Keeping track of what is going on with the latest DiD innovations.
doubleml4bounds
Double Machine Learning of Set-Identified Linear Models
Econ5121A
Econ5121A@CUHK. This is an open-source writing project.
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.
GenericML
R implementation of Generic Machine Learning Inference (Chernozhukov, Demirer, Duflo and Fernández-Val, 2020).
HDCQR_Paper
R Code for Paper "Inference for High Dimensional Censored Quantile Regression"
linearmodels
Add linear models including instrumental variable and panel data models that are missing from statsmodels.
MCPanel
Matrix Completion Methods for Causal Panel Data Models
PCI-China
Policy Change Index for China (PCI-China)
powershap
A power-full Shapley feature selection method.
pycop
Python library for multivariate dependence modeling with Copulas
scikit-garden
A garden for scikit-learn compatible trees
secret_event_study
event study, DID, confounders
secret_Staggered_DID
staggered DID econometrics
skgrf
scikit-learn compatible Python bindings for grf (generalized random forests) C++ random forest library
sumhdfe
Summary and diagnostic information for evaluating within-fixed-effect variation.
Surrogates-Replication-Code
Code to replicate GAIN application in Athey, Chetty, Imbens and Kang (2019) using simulated employment data
torchmdn
A simple module for Mixture Density Networks in Pytorch
treebased_synthetic_controls
This repo provides code to run the Tree-based Synthetic Control Estimator by Mühlbach & Nielsen (2020) (https://arxiv.org/abs/1909.03968)
zaminfluence
Tools in R for computing and using Z-estimator approximate influence functions.