Name: Zhang Chen
Type: User
Company: Shan Dong University of Finance and Economics
Bio: I am an econometrics teacher in Shan Dong University of Finance and Economics, focusing on statistics, econometrics and machine learning
Location: Jinan, Shandong, China
Zhang Chen's Projects
Agent-Based Complete Economy, the Python library that makes AB modelling easier..
Awesome Domain Adaptation Python Toolbox
R package to download project information from AidData 3.0 API
Approaching (Almost) Any Machine Learning Problem
Code and materials to reproduce adversarial RF paper
An index of algorithms for learning causality with data
PSL module that estimates partial-equilibrium behavioral responses to tax changes simulated by Tax-Calculator
Python translation (and extension) of the Tetrad java code.
Must-read papers and resources related to causal inference and machine (deep) learning
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
Implementation of Double Machine Learning
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。
Uplift modeling and causal inference with machine learning algorithms
This repository consolidates my teaching material for "Causal Machine Learning".
CausalModel implements widely used casual inference methods as well as an interference based method proposed by our paper.
An R package for causal discovery in heavy-tailed models
R package cfcausal
Code for paper "Conditional Variable Importance for Mixed Data" by Kristin Blesch, David S. Watson, Marvin N. Wright (2022)
Composite Indicators Framework for Business Cycle Analysis
Convolution-type Smoothed Quantile Regression
Convolution Smoothed Quantile Regression
Counterfact package for plotting counterfactual predictions
An R package to perform composite quantile factor analysis
A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python
Code for "Instrument Validity Tests with Causal Trees" MEA DP 1872 http://www.mea.mpisoc.mpg.de/uploads/user_mea_discussionpapers/1872_MEA_DP_05_2018.pdf