Causal inference is an important but controversial topic in the social sciences in that it is difficult to statistically control for all possible confounding variables. To address this concern, this monograph introduces a reference distribution of the confounding that is the product of two dependent correlation coefficients and illustrates how to use the reference distribution to investigate the robustness of a cause inference to the impact of a confounding variable. The methodology discussed in this monograph would also allow for multiple partial causes in the complex social phenomena under study, so as to inform causal inferences in the social sciences from statistical linear models.

This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (www.guilford.com/pan-materials).

The book compares the differences and similiarity of Singapore and China, in terms of economy, political system and examines the possibilites of cooperation in the future.