yannicdamm

yannicdamm's Stars

• cccneto/Ibamam

  Base de dados sobre multas e autuações ambientais do Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis - IBAMA.

  Language:R125

• Rachnog/Deep-Trading

  Algorithmic trading with deep learning experiments

  Language:OpenEdge ABL1.4k695

• rheilmayr/asm

  Replication data for ASM paper

  Language:Jupyter Notebook11

• rfsaldanha/microdatasus

  Download de dados do DataSUS e pré-processamento no R.

  Language:R22455

• gustavobio/brclimate

  Scrape Brazilian climate data

  Language:R208

• ipeaGIT/geobr

  Easy access to official spatial data sets of Brazil in R and Python

  Language:R794119

• wxwx1993/GPSmatching

  R Package for "Matching on generalized propensity scores with continuous exposures". An innovative approach for estimating causal effects using observational data in settings with continuous exposures, and a new framework for GPS caliper matching that jointly matches on both the estimated GPS and exposure levels to fully adjust for confounding bias.

  Language:R2916

• xiangzhou09/rbw

  Implementation of Residual Balancing Weights for Marginal Structural Models

  Language:R8

• py-why/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.

  Language:Jupyter Notebook3.8k713

• yixinsun1216/crossfit

  Implementation of Double/Debiased Machine Learning approach

  Language:R32

• MCKnaus/dmlmt

  Double Machine Learning for Multiple Treatments

  Language:R3912

• VC2015/DMLonGitHub

  Language:R2114

• KColangelo/DDML

  Language:Python2

• erayturkel/MLContinuous

  Code for the Project: "Machine Learning Methods for Causal Inference with Continuous Treatments"

  Language:R3

• lucasmation/microdadosBrasil

  Reads most common Brazilian public microdata (CENSO, PNAD, etc) easy and fast

  Language:R16459