Sufficient Covariate, Propensity Variable and Doubly Robust Estimation

Statistical causal inference from observational studies often requires adjustment for a possibly multi-dimensional variable, where dimension reduction is crucial. The propensity score, first introduced by Rosenbaum and Rubin, is a popular approach to such reduction. We address causal inference within Dawid's decision-theoretic framework, where it is essential to pay attention to sufficient covariates and their properties. We examine the role of a propensity variable in a normal linear model. We investigate both population-based and sample-based linear regressions, with adjustments for a multivariate covariate and for a propensity variable. In addition, we study the augmented inverse probability weighted estimator, involving a combination of a response model and a propensity model. In a linear regression with homoscedasticity, a propensity variable is proved to provide the same estimated causal effect as multivariate adjustment. An estimated propensity variable may, but need not, yield better precision than the true propensity variable. The augmented inverse probability weighted estimator is doubly robust and can improve precision if the propensity model is correctly specified

Propensity Score Analysis with Matching Weights

The propensity score analysis is one of the most widely used methods for studying the causal treatment effect in observational studies. This paper studies treatment effect estimation with the method of matching weights. This method resembles propensity score matching but offers a number of new features including efficient estimation, rigorous variance calculation, simple asymptotics, statistical tests of balance, clearly identified target population with optimal sampling property, and no need for choosing matching algorithm and caliper size. In addition, we propose the mirror histogram as a useful tool for graphically displaying balance. The method also shares some features of the inverse probability weighting methods, but the computation remains stable when the propensity scores approach 0 or 1. An augmented version of the matching weight estimator is developed that has the double robust property, i.e., the estimator is consistent if either the outcome model or the propensity score model is correct. In the numerical studies, the proposed methods demonstrated better performance than many widely used propensity score analysis methods such as stratification by quintiles, matching with propensity scores, and inverse probability weighting

Knowledge-centered culture and knowledge sharing: the moderator role of trust propensity

Purpose: This research aims to evaluate if knowledge-centered culture (KCC) fosters knowledge sharing equally across employees with different levels of trust propensity, an enduring individual characteristic. Design/methodology/approach: A cross-sectional questionnaire study was conducted with 128 US-based employees. Findings: The authors found that KCC only promoted knowledge sharing in individuals with high levels of trust propensity. For individuals with low levels of trust propensity, KCC had no effect on knowledge sharing. Research limitations/implications: The authors focused exclusively on trust propensity as a moderator. Future research could analyze the role of other enduring individual differences in the relationship between KCC and knowledge sharing. Practical implications: A KCC may be inefficient in promoting knowledge sharing in employees with low propensity to trust. Recruitment and selection of individuals with a high propensity to trust is a possible solution to enhance the association between KCC and knowledge sharing in organizations. Originality/value: By identifying an enduring individual characteristic that shapes the relationship between KCC and knowledge sharing, the authors move toward the development of a contingent view of KCC and show that KCC fosters knowledge sharing differently across employees

The R&D-patent relationship: An industry perspective

This paper re-visits the empirical failure to establish a clear link between R&D efforts and patent counts at the industry level. It is claimed that the “propensity-to-patent” concept should be split into an “appropriability propensity” and a “strategic propensity”. The empirical contribution is based on a unique panel dataset composed of 18 industries in 19 countries over 19 years. The results confirm that the R&D-patent relationship is affected by research productivity, appropriability propensity and strategic propensity factors. The observed increase in the propensity to file for patents is much stronger for supranational (that is, triadic or regional) patents than for priority filings, suggesting that the current patent hype is essentially the result of a globalization phenomenon.Propensity to patent; strategic propensity; appropriability; research productivity