A statistical approach for meta-analyzing adjusted and unadjusted estimates from epidemiological cohorts/studies.
R functions to compute bias-adjusted treatment effect when selection on unobservables is proportional to selection on observables
R package for regression sensitivity analysis to omitted variable bias: identified sets and breakdown points following Diegert, Masten & Poirier (2026) and Oster (2019).
Generate longitudinal data and demonstrate the bias caused by failed fixed effects assumptions.
Research code for "How Biased Is Your Regression Model?". Unifies omitted variable bias (OVB) and ML fairness for telecom churn prediction. Includes FairLogisticRegression, segment disparity metrics, and a Bias-Aware MLOps architecture.
This repo contains various projects and assignments I worked on in my Economics Courses
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