Front-door Adjustment Methods for Causal Inference — Front-door Versus Back-door Adjustment with Unmeasured Confounding: Bias Formulas for Front-door and Hybrid Adjustments (with Adam Glynn) In this paper, we develop bias formulas for front-door and frontdoor/back-door hybrid estimators that utilize mechanistic information from post-treatment variables under general patterns... Read more
Evaluating U.S. Social Security Administration Forecasts — The accuracy of U.S. Social Security Administration (SSA) demographic and financial forecasts is crucial for the solvency of its Trust Funds, government programs comprising greater than 50% of all federal government expenditures, industry decision making, and the evidence base of... Read more
qualCI: Causal Inference with Qualitative and Ordinal Information on Outcomes — Exact one-sided p-values and confidence intervals for an outcome variable defined on an interval measurement scale with only qualitative and ordinal information available. Co-authors of the package are Adam Glynn and Nahomi Ichino. Download the package from CRAN Browse the... Read more
panelAR: Estimation of Linear AR(1) Panel Data Models with Cross-Sectional Heteroskedasticity and/or Correlation — The package estimates linear models on panel data structures in the presence of AR(1)-type autocorrelation as well as panel heteroskedasticity and/or contemporaneous correlation. First, AR(1)-type autocorrelation is addressed via a two-step Prais-Winsten feasible generalized least squares (FGLS) procedure, where the... Read more