observational-data

"Evaluating Digital Agriculture Recommendations with Causal Inference". It was accepted and presented in the special track on Artificial Intelligence for Social Impact, AAAI-23

A Python package to access, download, view, and manipulate Cassini RADAR images in one place

Python Request Engine for Virtual Interferometric Survey

Repo for the paper "Survey calibration for causal inference: a simple method to balance covariate distributions”

This repository provides the workshop materials for latent variable models applied to data on wartime sexual violence. This workshop was previously taught at Ashoka University (2018) and Michigan State University (2019).

This repository provides the Online Appendix, replication data and materials for Krüger & Nordås, A latent variable approach to measuring wartime sexual violence, in Journal of Peace Research

Matching Methods for Time-Varying Observational Studies, in R

Reproduce results from the paper "Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis. K. Chalkou et. al. Diagn Progn Res . 2021 Oct 27;5(1):17. doi: 10.1186/s41512-021-00106-6."

Processing and analysing data gathered by mammal watching.

A prospective registry assessing hepatic enzyme derangements and inpatient outcomes in hospitalized COVID-19 patients; features GCP and 21 CFR Part 11-compliant eCRF design, data dictionary, and validation workflows.

Propensity scores in complex surveys

Causal inference toolkit in Python: DiD, Synthetic Control and BSTS with ground-truth validation and placebo tests; reports 69% cross-method disagreement rather than hiding it; FastAPI service, Streamlit demo, Docker + GitHub Actions CI at >=90% coverage, pytest suite.

App for NWPA

Casual relationship between health insurance coverage and BMI.

Advanced Observational Astronomy, Spring 2024

Causal effect of quitting smoking on weight gain in the NHEFS cohort, using causal forests, IPW and AIPW, validated against Hernán and Robins.

SAS and R Assignments completed at Undergraduate Applied Statistics Program

Comparative simulation study of TMLE and C-TMLE estimators for causal inference in high-dimensional observational data · R

Re-reduced classification spectra from the ePESSTO+ survey, used in the paper:
"No rungs attached: A distance-ladder free determination of the Hubble constant through type II supernova spectral modelling."
by C. Vogl et al. (2024).

Applied data analysis and causal inference projects in social science (R & Python), featuring experimental, observational, and reproducible research workflows