advancehs Goto Github PK

addana

address parsers

addanase

address-parse

🌏对国内地址地区进行智能解析，提取关键数据，如有识别不准的地址请Issues

advancehs.github.io

personal website

allocating-china-s-co2-emissions-based-on-economic-welfare-gains-from-environmental-externalities

This repository provides code for responsibility allocation model in the article Allocating China’s CO2 Emissions based on Economic Welfare Gains from Environmental Externalities, as well as template data to guide users to use this model.

aps_vine_copula_sfa

areacity-jsspider-statsgov

省市区县乡镇三级或四级城市数据，带拼音标注、坐标、行政区域边界范围；2023年06月04日最新采集，提供csv格式文件，支持在线转成多级联动js代码、通用json格式，提供软件转成shp、geojson、sql、导入数据库；带浏览器里面运行的js采集源码，综合了中华人民共和国民政部、国家统计局、高德地图、腾讯地图行政区划数据

awesome-causality-algorithms

An index of algorithms for learning causality with data

causalml

Uplift modeling and causal inference with machine learning algorithms

china-administrative-division

中华人民共和国行政区划代码 - Codes for the administrative divisions of the People's Republic of China

chinaadmindivisonshp

**行政区划矢量图，ESRI Shapefile格式，共四级：国家、省/直辖市、市、区/县。关键字：**行政区划图；**地图；**行政区；**行政区地图；行政区地图；行政区；行政区划；地图；矢量数据；矢量地理数据；省级；直辖市；市级；区/县级；行政区划图。

commonly-used-control-variables

Datasets of commonly used control variables. By Shutter Zor

coordtransform

提供了百度坐标（BD09）、国测局坐标（火星坐标，GCJ02）、和WGS84坐标系之间的转换

datasciencestudynotes

这个仓库保管从（数据科学学习手札69）开始的所有代码、数据等相关附件内容

ddml

Double/Debiased Machine Learning implementation for Stata

dea

数据包络分析

did

Difference in Differences with Multiple Periods and Variation in Treatment Timing

did-1

Keeping track of what is going on with the latest DiD innovations.

did_imputation

Event studies: robust and efficient estimation, testing, and plotting

earth-analytics-python-env

A conda environment and docker container to support using earth and environmental data in Python.

easymore

EASYMORE; EArth SYstem MOdeling REmapper

eatpy

Efficiency Analysis Trees in Python

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.

energy-code-release-2020

Supplemental material for "Estimating a Social Cost of Carbon for Global Energy Consumption" (Rode et al., 2021) https://doi.org/10.1038/s41586-021-03883-8.

eventstudyinteract

fixedeffectmodel

Fixedeffectmodel: panel data modeling in Python

geodata

gtfpch

Total Factor Productivity with Undesirable Outputs in Stata

hydromt

HydroMT: Automated and reproducible model building and analysis

lmdi

LMDI 数据分析