We investigate the estimation of subgroup treatment effects with observational data. Existing propensity score matching and weighting methods are mostly developed for estimating overall treatment effect. Although the true propensity score should balance covariates for the subgroup populations, the estimated propensity score may not balance covariates for the subgroup samples. We propose the subgroup balancing propensity score (SBPS) method, which selects, for each subgroup, to use either the overall sample or the subgroup sample to estimate propensity scores for units within that subgroup, in order to optimize a criterion accounting for a set of covariate-balancing conditions for both the overall sample and the † Jing Dong subgroup samples. We develop a stochastic search algorithm for the estimation of SBPS when the number of subgroups is large. We demonstrate through simulations that the SBPS can improve the performance of propensity score matching in estimating subgroup treatment effects. We then apply the SBPS method to data from the Italy Survey of Household Income and Wealth (SHIW) to estimate the treatment effects of having debit card on household consumption for different income groups.

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Background In online medical consulting platforms, physicians can get both economic and social returns by offering online medical services, such as answering questions or sharing health care knowledge with patients. Physicians’ online prosocial behavior could bring many benefits to the health care industry. Monetary incentives could encourage physicians to engage more in online medical communities. However, little research has studied the impact of monetary incentives on physician prosocial behavior and the heterogeneity of this effect. Objective This study aims to explore the effects of monetary incentives on physician prosocial behavior and investigate the moderation effects of self-recognition and recognition from others of physician competence. Methods This study was a fixed-effect specification-regression model based on a difference-in-differences design with robust standard errors clustered at the physician level using monthly panel data. It included 26,543 physicians in 3851 hospitals over 133 months (November 2006-December 2017) from a leading online health care platform in China. We used the pricing strategy of physicians and satisfaction levels to measure their own and patients’ degree of recognition, respectively. Physicians’ prosocial behavior was measured by free services offered. Results The introduction of monetary incentives had a positive effect on physician prosocial behavior (β=1.057, P<.01). Higher self-recognition and others’ recognition level of physician competence increased this promotion effect (γ=0.275, P<.01 and γ=0.325, P<.01). Conclusions This study explored the positive effect of the introduction of monetary incentives on physician prosocial behavior. We found this effect was enhanced for physicians with a high level of self-recognition and others’ recognition of their competence. We provide evidence of the effect of monetary incentives on physicians’ prosocial behaviors in the telemedicine markets and insight for relevant stakeholders into how to design an effective incentive mechanism to improve physicians’ prosocial engagements.

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As the developments of new techniques, mobile social networks have been built wildly. To obtain and spread information over mobile social networks efficiently, the influence maximization problem is to find a seed nodes set with limited size such that it can influence as many nodes as possible. Previous works ignore the dynamic influence phenomenon of diffusing information on mobile social networks. In this article, we propose a new model to express the procedure of diffusing information under the existence of dynamic influence. Theoretical analysis shows that the influence maximization problem under new model is non-deterministic polynomial-time hard, and efficient approximation algorithm is proposed. Experimental studies on real data sets show that the new model can process dynamic influence well in the diffusing information procedure, and the proposed algorithms can solve the influence maximization problem on new model efficiently.