mehmet-sari Goto Github PK

990_metadata

Code for adding metadata to IRS 990 change logs.

abms-for-causal-inference

Code for: Murray EJ, Robins JM, George R. Seage III, Freedberg KA, Hernan MA. A Comparison of Agent-Based Models and the Parametric G-Formula for Causal Inference. American Journal of Epidemiology. 2017;186(2):131-42.

applied-methods-phd

Repo for Yale Applied Empirical Methods PHD Course

auto-gpt

An experimental open-source attempt to make GPT-4 fully autonomous.

bda_course_aalto

Bayesian Data Analysis course at Aalto

blp-demand

estimate BLP demand model in Matlab using state-of-the-art techniques

bootcamp2018

Repository of syllabi, lecture notes, Jupyter notebooks, code, and problem sets for OSM Lab Boot Camp 2018

bootcamp2019

Repository of syllabi, lecture notes, Jupyter notebooks, code, and problem sets for OSE Lab Boot Camp 2019

category_theory_machine_learning

List of papers studying machine learning through the lens of category theory

causalgraphs_latex

Drawing graphical models for causal inference using LaTeX

communitybenefits_sdoh

This is the project that examines the community benefit provision by hospitals

covid-19-data

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

dowhy

DoWhy is a Python library that makes it easy to estimate causal effects. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

dreambooth-stable-diffusion

Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion (tweaks focused on training faces)

dse2019

Teaching materials from DSE2019 summer school at Chicago Booth

dse2022aus

Lecture materials for the DSE2022AUS at ANU, Canberra, Australia

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.

econometric_theory_slides

Slides for A Primer in Econometric Theory

exercises_applications

Datasets for lecture exercises

fingpt

Data-Centric FinGPT. Open-source for open finance! Revolutionize 🔥 We'll soon release the trained model.

grad-io

Graduate Empirical Industrial Organization

hcup_quality_indicator

It includes hcup quality indicator stata code.

hospital-cost-reports

Code to process the CMS hospital cost reports (HCRIS)

introcausality

Class materials for "Economics, Causality, and Analytics"

islr-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

jupytext

Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

kepler.gl

lectures

Lecture notes for EC 607

machine-learning-book

Code Repository for Machine Learning with PyTorch and Scikit-Learn

mathematical-python

Introduction to Mathematical Computing with Python and Jupyter