davidnathanlang

davidnathanlang's Stars

• owid/covid-19-data

  Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data

  Language:Python5.7k1509363.6k

• 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 Notebook3.9k76572719

• facebookresearch/MUSE

  A library for Multilingual Unsupervised or Supervised word Embeddings

  Language:Python3.2k98171553

• HendrikStrobelt/LSTMVis

  Visualization Toolbox for Long Short Term Memory networks (LSTMs)

  Language:Jupyter Notebook1.2k5359256

• facebookexperimental/Robyn

  Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

  Language:Jupyter Notebook1.2k59732344

• lvapeab/nmt-keras

  Neural Machine Translation with Keras

  Language:Python53326123130

• cynkra/dm

  Working with relational data models in R

  Language:R502963249

• thushv89/attention_keras

  Keras Layer implementation of Attention for Sequential models

  Language:Python4451232266

• kochbj/Deep-Learning-for-Causal-Inference

  Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2 and Pytorch.

  30411967

• rmcelreath/causal_salad_2021

  One day course on causal inference, MPI-EVA 9 September 2021

  Language:R24411070

• dlab-berkeley/Computational-Social-Science-Training-Program

  This course is a rigorous, year-long introduction to computational social science. We cover topics spanning reproducibility and collaboration, machine learning, natural language processing, and causal inference. This course has a strong applied focus with emphasis placed on doing computational social science.

  Language:Jupyter Notebook22314897

• ThinkR-open/fusen

  Inflate your package from a simple flat Rmd / Qmd

  Language:R163715410

• inovex/justcause

  💊 Comparing causality methods in a fair and just way.

  Language:Jupyter Notebook138112515

• mattansb/Structural-Equation-Modeling-foR-Psychologists

  Lesson files used in the Structural Equation Modeling for Psychologists.

  Language:R13681930

• ja-thomas/autoxgboost

  autoxgboost - Automatic tuning and fitting of xgboost

  Language:R121186019

• edunford/tidysynth

  A tidy implementation of the synthetic control method in R

  Language:R10073314

• behrman/ros

  Regression and Other Stories - Tidyverse Examples

  Language:R992025

• prodriguezsosa/EmbeddingRegression

  Repository for paper "Embedding Regression: Models for Context-Specific Description and Inference"

  87735

• koheiw/seededlda

  LDA for semisupervised topic modeling

  Language:R7372915

• trinker/entity

  Easy named entity extraction

  Language:R54546

• shahrukhx01/siamese-nn-semantic-text-similarity

  A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task, including architectures such as: Siamese LSTM Siamese BiLSTM with Attention Siamese Transformer Siamese BERT.

  Language:Python48217

• dave-harrington/survival_workshop

  Survival analysis workshop using R

  Language:R472026

• kwuthrich/scinference

  Inference for synthetic controls

  Language:R38223

• nppackages/scpi

  Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.

  Language:R302010

• ecase2/SCF2019summarystats

  I replicate and extend summary statistics in the Federal Reserve's 2019 Survey of Consumer Finances Interactive Chart.

  Language:Stata8100

• paulgp/google_scholar_share

  Language:Python8216

• odelmarcelle/sentopics

  Language:R7132

• ben-domingue/imv

  Language:R4391

• stanford-datalab/dcl

  Tools for Data Challenge Lab

  Language:R2233

• yyang24/tree-based-synthetic-control

  This repository contains a R suite to construct prediction intervals to estimate the casual effect of policy changes in post-treatment periods, combining the conventional synthetic control methods with random forest framework. This method is applied to extend the seminal work of Abadie et al. (2010) on the influence of California's tabacco control program in 1988.

  Language:R110