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

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

Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs

This paper uses propensity score methods to address the question: how well can an observational study estimate the treatment impact of a program? Using data from Lalonde's (1986) influential evaluation of non-experimental methods, we demonstrate that propensity score methods succeed in estimating the treatment impact of the National Supported Work Demonstration. Propensity score methods reduce the task of controlling for differences in pre-intervention variables between the treatment and the non-experimental comparison groups to controlling for differences in the estimated propensity score (the probability of assignment to treatment, conditional on covariates). It is difficult to control for differences in pre-intervention variables when they are numerous and when the treatment and comparison groups are dissimilar, whereas controlling for the estimated propensity score, a single variable on the unit interval, is a straightforward task. We apply several methods, such as stratification on the propensity score and matching on the propensity score, and show that they result in accurate estimates of the treatment impact.

Application of the Generalized Propensity Score. Evaluation of public contributions to Piedmont enterprises.

In this article, we apply a generalization of the propensity score of Rosenbaum and Rubin (1983b). Techniques based on the propensity score have long been used for causal inference in observational studies for reducing bias caused by non-random treatment assignment. In last years, Joffe and Rosenbaum (1989) and Imbens and Hirano (2000) suggested two possible extensions to standard propensity score for ordinal and categorical treatments respectively. Propensity score techniques, allowing for continuous treatments effect evaluation, were, instead, recently proposed by Van Dick Imai (2003) and Imbens and Hirano (2004). We refer to Imbens' approach for the use of the generalized propensity score, to widen its application for continuous treatment regimes.