Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

Randomized clinical trials (RCT) are accepted as the gold-standard approaches to measure
effects of intervention or treatment on outcomes. They are also the designs of choice for …

Propensity score matching (PSM) is a popular method in clinical researches to create a
balanced covariate distribution between treated and untreated groups. However, the …

The synthetic control method (SCM) is a popular approach for estimating the impact of a
treatment on a single unit in panel data settings. The “synthetic control” is a weighted …

Background: Patients with end-stage kidney disease (ESKD) on dialysis were excluded from
clinical trials of direct oral anticoagulants for atrial fibrillation (AF). Recent data have raised …

Objective: We extend the literature on comorbidity measurement by developing 2 indices,
based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported …

This paper presents a novel nonlinear regression model for estimating heterogeneous
treatment effects, geared specifically towards situations with small effect sizes …

Background: Comorbidity adjustment is an important component of health services research
and clinical prognosis. When adjusting for comorbidities in statistical models, researchers …

Objective Examining covariate balance is the prescribed method for determining the degree
to which propensity score methods should be successful at reducing bias. This study …

When estimating causal effects using observational data, it is desirable to replicate a
randomized experiment as closely as possible by obtaining treated and control groups with …