Many digital platforms have employed free-content promotion strategies to deal with the high uncertainty levels regarding digital content products.However,the diversity of digital content products and user heterogenei...Many digital platforms have employed free-content promotion strategies to deal with the high uncertainty levels regarding digital content products.However,the diversity of digital content products and user heterogeneity in content preference may blur the impact of platform promotions across users and products.Therefore,free-content promotion strategies should be adapted to allocate marketing resources optimally and increase revenue.This study develops personal-ized free-content promotion strategies based on individual-level heterogeneous treatment effects and explores the causes of their heterogeneity,focusing on the moderating effect of user engagement-related variables.To this end,we utilize ran-dom field experimental data provided by a top Chinese e-book platform.We employ a framework that combines machine learning with econometric causal inference methods to estimate individual treatment effects and analyze their potential mechanisms.The analysis shows that,on average,free-content promotions lead to a significant increase in consumer pay-ments.However,the higher the level of user engagement,the lower the payment lift caused by promotions,as more-engaged users are more strongly affected by the cannibalization effect of free-content promotion.This study introduces a novel causal research design to help platforms improve their marketing strategies.展开更多
This study investigates the persistent academic impacts of the Head Start program,a federal government-funded early childhood intervention,using data from the Early Childhood Longitudinal Study-Kindergarten Cohort(ECL...This study investigates the persistent academic impacts of the Head Start program,a federal government-funded early childhood intervention,using data from the Early Childhood Longitudinal Study-Kindergarten Cohort(ECLSK).Bayesian Additive Regression Trees(BARTs)are the primary methodology used,and average,conditional,and individual-level treatment impacts on children’s mathematics achievement are estimated.BART estimates a negative Average Treatment Effect(ATE)of−1.5421 with increasingly larger adverse effects for children with higher Socioeconomic Status(SES),suggesting diminishing marginal returns.This finding demonstrates the strength of BART to detect nonlinear moderation patterns that are evasive to conventional models.It also implies that Head Start and other preschool interventions will yield greater policy returns when targeted at low-SES children,in order to enable more efficient and fair distribution of public funds.For comparison,Causal Forest estimates a larger ATE(−2.4340)and determines SES to be the overarching moderator,while Propensity Score Matching offers a conservative estimate(−1.2606)without considering effect heterogeneity.These findings underscore the utility of BART in estimating subtle,SES-varying effects of Head Start,and suggest the potential value of more targeted intervention strategies guided by adaptive causal inference.展开更多
Demand-side flexibility is crucial to balancing supply and demand,as renewable energy sources are increasingly integrated into the energy mix,and heating and transport systems are becoming more and more electrified.Hi...Demand-side flexibility is crucial to balancing supply and demand,as renewable energy sources are increasingly integrated into the energy mix,and heating and transport systems are becoming more and more electrified.Historically,this balancing has been managed from the supply side.However,the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants,increasing the importance of demand response(DR)techniques to achieve the required flexibility.Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer,a task complicated by numerous influencing variables.Based on a top-down approach,this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads.We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads,subject to flexibility,which is simulated by a virtual battery model.The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions.The results demonstrate the model’s ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption.The model achieves a mean absolute percentage error(MAPE)lower than 17.0%,comparable to the accuracy without flexibility.The results obtained are compared with a direct data-driven approach,demonstrating the validity and effectiveness of our model.展开更多
基金supported by the Anhui Postdoctoral Scientific Research Program Foundation(2022B579).
文摘Many digital platforms have employed free-content promotion strategies to deal with the high uncertainty levels regarding digital content products.However,the diversity of digital content products and user heterogeneity in content preference may blur the impact of platform promotions across users and products.Therefore,free-content promotion strategies should be adapted to allocate marketing resources optimally and increase revenue.This study develops personal-ized free-content promotion strategies based on individual-level heterogeneous treatment effects and explores the causes of their heterogeneity,focusing on the moderating effect of user engagement-related variables.To this end,we utilize ran-dom field experimental data provided by a top Chinese e-book platform.We employ a framework that combines machine learning with econometric causal inference methods to estimate individual treatment effects and analyze their potential mechanisms.The analysis shows that,on average,free-content promotions lead to a significant increase in consumer pay-ments.However,the higher the level of user engagement,the lower the payment lift caused by promotions,as more-engaged users are more strongly affected by the cannibalization effect of free-content promotion.This study introduces a novel causal research design to help platforms improve their marketing strategies.
文摘This study investigates the persistent academic impacts of the Head Start program,a federal government-funded early childhood intervention,using data from the Early Childhood Longitudinal Study-Kindergarten Cohort(ECLSK).Bayesian Additive Regression Trees(BARTs)are the primary methodology used,and average,conditional,and individual-level treatment impacts on children’s mathematics achievement are estimated.BART estimates a negative Average Treatment Effect(ATE)of−1.5421 with increasingly larger adverse effects for children with higher Socioeconomic Status(SES),suggesting diminishing marginal returns.This finding demonstrates the strength of BART to detect nonlinear moderation patterns that are evasive to conventional models.It also implies that Head Start and other preschool interventions will yield greater policy returns when targeted at low-SES children,in order to enable more efficient and fair distribution of public funds.For comparison,Causal Forest estimates a larger ATE(−2.4340)and determines SES to be the overarching moderator,while Propensity Score Matching offers a conservative estimate(−1.2606)without considering effect heterogeneity.These findings underscore the utility of BART in estimating subtle,SES-varying effects of Head Start,and suggest the potential value of more targeted intervention strategies guided by adaptive causal inference.
基金the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No.957755,project SENDER:Sustainable consumer engagement and demand response.
文摘Demand-side flexibility is crucial to balancing supply and demand,as renewable energy sources are increasingly integrated into the energy mix,and heating and transport systems are becoming more and more electrified.Historically,this balancing has been managed from the supply side.However,the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants,increasing the importance of demand response(DR)techniques to achieve the required flexibility.Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer,a task complicated by numerous influencing variables.Based on a top-down approach,this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads.We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads,subject to flexibility,which is simulated by a virtual battery model.The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions.The results demonstrate the model’s ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption.The model achieves a mean absolute percentage error(MAPE)lower than 17.0%,comparable to the accuracy without flexibility.The results obtained are compared with a direct data-driven approach,demonstrating the validity and effectiveness of our model.
