Pinned Repositories
bayesian-final
bayesian_group
group discussion repo
blogocosgrove
Build a Jekyll blog in minutes, without touching the command line.
buildingblocks-git
cv
My CV built using RMarkdown and the pagedown package.
datawrangling1
docsy-site
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.
p8105_final-project
surveys-final
Final project repository for Analysis of Health Surveys.
lauracosgrove's Repositories
lauracosgrove/p8105_final-project
lauracosgrove/surveys-final
Final project repository for Analysis of Health Surveys.
lauracosgrove/bayesian-final
lauracosgrove/bayesian_group
group discussion repo
lauracosgrove/blogocosgrove
Build a Jekyll blog in minutes, without touching the command line.
lauracosgrove/buildingblocks-git
lauracosgrove/cv
My CV built using RMarkdown and the pagedown package.
lauracosgrove/datawrangling1
lauracosgrove/docsy-site
lauracosgrove/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.
lauracosgrove/final_oasis_ds2
using OASIS data for a series of classification tasks
lauracosgrove/genie_mutations
GENIE mutations
lauracosgrove/iAdapt-shiny
The Shiny app for the iAdapt project
lauracosgrove/lauracosgrove.github.io
My personal site
lauracosgrove/linear_models
lauracosgrove/longitudinal-final
lauracosgrove/midterm_P8106
midterm project with heart disease data
lauracosgrove/mimic-code
MIMIC Code Repository: Code shared by the research community for the MIMIC-III database
lauracosgrove/p8105_hw1_lec2197
Homework 1 for P8105
lauracosgrove/p8105_hw2_lec2197
Homework 2 for P8105
lauracosgrove/p8105_hw3_lec2197
Homework 3 for P8105
lauracosgrove/p8105_hw4_lec2197
Homework 4 for P8105
lauracosgrove/p8105_hw5_lec2197
Homework 5 for P8105
lauracosgrove/p8105_hw6_lec2197
Homework 6 for P8105
lauracosgrove/personal-site
lauracosgrove/sensitivitypuc
R package to perform the sensitivity analysis by the proportion of unmeasured confounding.
lauracosgrove/website-example
example for class