Pinned Repositories
applied-methods-phd
Repo for Yale Applied Empirical Methods PHD Course
ContactPigeon
ContactPigeon Data Studio Connector
culture-fairness
datacamp_projects
Datacamp projects completed
datagovgR
R Wrapper Package for the data.gov.gr API
datasharing
The Leek group guide to data sharing
DiD
Keeping track of what is going on with the latest DiD innovations.
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.
ImportRegex
Google sheets custom function for web scraping.
pca_inR
A notebook for step-by-step Principal Component Analysis using R
elenigvasilaki's Repositories
elenigvasilaki/datagovgR
R Wrapper Package for the data.gov.gr API
elenigvasilaki/pca_inR
A notebook for step-by-step Principal Component Analysis using R
elenigvasilaki/ImportRegex
Google sheets custom function for web scraping.
elenigvasilaki/applied-methods-phd
Repo for Yale Applied Empirical Methods PHD Course
elenigvasilaki/ContactPigeon
ContactPigeon Data Studio Connector
elenigvasilaki/culture-fairness
elenigvasilaki/datacamp_projects
Datacamp projects completed
elenigvasilaki/datasharing
The Leek group guide to data sharing
elenigvasilaki/DiD
Keeping track of what is going on with the latest DiD innovations.
elenigvasilaki/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.
elenigvasilaki/kmeans_cluster
Customer segmentation using k-means clustering in python
elenigvasilaki/lectures
Lecture notes for EC 607
elenigvasilaki/Machine-Learning-with-R-datasets
Formatted datasets for Machine Learning With R by Brett Lantz
elenigvasilaki/pydatagovgr
A Pythonic client for the official https://data.gov.gr API.