Confounding can cause substantial bias in nonexperimental studies that aim to estimate
causal effects. Propensity score methods allow researchers to reduce bias from measured …

Propensity score weighting and outcome regression are popular ways to adjust for observed
confounders in epidemiologic research. Here, we provide an introduction to matching …

Causal inference from observational data is a vital problem, but it comes with strong
assumptions. Most methods assume that we observe all confounders, variables that affect …

Supplement to “Propensity score weighting for causal inference with multiple treatments”.
Supplement A: On Transitivity. We provide a detailed discussion on transitivity of the target …

Continuous treatments propensity scoring remains understudied as the majority of methods
are focused on the binary treatment setting. Current propensity score methods for …

Causal inference has numerous real-world applications in many domains, such as health
care, marketing, political science, and online advertising. Treatment effect estimation, a …

Estimating counterfactual outcome of different treatments from observational data is an
important problem to assist decision making in a variety of fields. Among the various forms of …

We address the estimation of conditional average treatment effects (CATEs) for structured
treatments (eg, graphs, images, texts). Given a weak condition on the effect, we propose the …

Background: It is unclear whether anticoagulant type is associated with the risk for
osteoporotic fracture, a deleterious complication of anticoagulants among patients with atrial …

When does information technology (IT)–business alignment matter most for leveraging IT
investment for firm performance? Drawing on dynamic capabilities theory, we posit that firms …