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 …

The propensity score is a common tool for estimating the causal effect of a binary treatment
in observational data. In this setting, matching, subclassification, imputation or inverse …

More comprehensive than other texts, this new book covers the classic and cutting edge
multivariate techniques used in today's research. Ideal for courses on multivariate …

Propensity‐score matching is increasingly being used to reduce the confounding that can
occur in observational studies examining the effects of treatments or interventions on …

Entrepreneurship researchers have documented that early stage startups rely on signals to
demonstrate the transitions in their identities that they must make when they cross …

The propensity score is the probability of treatment assignment conditional on observed
baseline characteristics. The propensity score allows one to design and analyze an …

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive
manual for the applied researcher who wants to perform data analysis using linear and …

Researchers have long struggled to identify causal effects in nonexperimental settings.
Many recently proposed strategies assume ignorability of the treatment assignment …

In this second edition of Counterfactuals and Causal Inference, completely revised and
expanded, the essential features of the counterfactual approach to observational data …

Propensity‐score matching is frequently used to estimate the effect of treatments, exposures,
and interventions when using observational data. An important issue when using propensity …