Propensity scores in complex surveys
-
Updated
Oct 9, 2023 - JavaScript
Propensity scores in complex surveys
Android app infrastructure for observing various web applications. For example mimicking a special app to get insights how users are using it. Especially interesting for the usage of design patterns.
Server infrastructure for observing study participants actions from a corresponding app. Ability to adjust to the needed requirements and to do automated basic statistical analysis.
An analysis of weather patterns in over 500 countries, using python-api and matplotlib
This repository contains code for curating data and testing hypotheses relating to the College-to-Work Transition study.
This is Loan Robinson Github Page, which includes all tutorials for education purpose.
This program uses data from JPL Horizons to calculate when that object will be at opposition. It automates finding oppositions for creating observation plans for telescopic observations.
Reproducibility materials for "Cross-Screening in Observational Studies that Test Many Hypotheses" by Qingyuan Zhao, Dylan S. Small & Paul R. Rosenbaum
Problem Set 1 from STA304 Surveys, Sampling and Observational Data [Data used in the analysis was obtained from the Toronto Open Data Portal (https://open.toronto.ca/)]
An application for creating, validating, reusing and extending sets of clinical codes.
Population-based observational studies of the epidemiology of infectious diseases
Virtual coach Jamie for setting physical activity plans with users that users are committed to.
Prioritize variables in observational study design through the joint variable importance plot
Casual relationship between health insurance coverage and BMI.
This github repository contains the code for the chatbot Steph that is created for the thesis project: Using Reinforcement Learning to Personalize Daily Step Goals for a Collaborative Dialogue with a Virtual Coach.
Codes for "Evidence Factors from Multiple, Possibly Invalid, Instrumental Variables"
Presentation slides for "A Bayesian Causal Inference Approach in Observational Studies with Missingness in Covariates and Outcomes".
Code for the paper "Hidden yet quantifiable: A lower bound for confounding strength using randomized trials"
Repository for course work done in CIS 443: User Interfaces.
Add a description, image, and links to the observational-study topic page so that developers can more easily learn about it.
To associate your repository with the observational-study topic, visit your repo's landing page and select "manage topics."