Research

External Cohort Reweighting via ESS-Maximization

A novel statistical framework for borrowing information from external cohorts to augment inference in a target study. The core contribution is an optimization-based reweighting scheme that identifies importance weights for the external cohort by maximizing effective sample size (ESS) subject to covariate balance constraints, producing weights that make external data as informative as possible for inference in the target population while explicitly controlling for distributional shift between populations.

Weighted Multi-Study Analysis Package

An R package for causal inference across multiple observational studies, each containing multiple groups, using a unified balancing weight framework. The package implements three weighting approaches (including the novel FLEXOR method that maximizes effective sample size through iterative optimization) to create covariate-balanced pseudo-populations that enable valid estimation of group-specific potential outcome features (means, medians, standard deviations) under study-specific unconfoundedness.

Simulation Infrastructure for Causal Inference

An R package for constructing, simulating, and evaluating causal data-generating processes with known ground truth. Designed for benchmarking causal estimators, stress-testing assumption violations, and building reproducible simulation studies. Variable roles (confounders, instruments, mediators, effect modifiers) and assumption violations are first-class concepts in the API.

Interrupted Time Series Analysis

Analyzes the causal impact of the COVID-19 pandemic on hospital-onset MRSA infection rates using a 10-year national panel dataset spanning 2015–2024. Identification uses interrupted time series with state fixed effects to estimate pandemic disruption to infection control trends, controlling for pre-existing state-level trajectories. The framework was extended to evaluate state-level policy and demographic moderators of pandemic impact.

An Event-Study Difference-in-Differences Analysis

Estimates the causal effect of ACA Medicaid expansion on insurance coverage among low-income adults using individual-level ACS PUMS microdata spanning 2010–2023. Compares 2014 expansion states against never-expanding states via a two-way fixed effects event-study DiD design (fixest::feols), recovering year-by-year treatment effect estimates and state-level counterfactuals. Finds that expansion states achieved persistently lower uninsured rates post-2014, with an estimated 35,000 low-income adults left uninsured in Texas alone due to non-expansion.

Population Health Analytics Platform

Interactive population health analytics platform built on NHANES data. Presents large-scale health and behavioral survey data through polished, navigable visualizations covering health behaviors, chronic conditions, and social determinants of health.

causal-methods.com

Interactive methods reference covering the major applied causal inference estimators. Each method includes identification assumptions, estimation strategy, robustness considerations, and tabbed R and Python implementation code. Includes an estimator decision tree.

dag-studio.com

Interactive browser-based tool for constructing and reasoning about directed acyclic graphs as graphical causal models. Supports visual DAG building, automatic identification of adjustment sets, and backdoor path analysis.