Determining the causal effect of special education is a critical topic when mak-ing educational policy that focuses on student achievement.However,current special education research is facing challenges from persisten...Determining the causal effect of special education is a critical topic when mak-ing educational policy that focuses on student achievement.However,current special education research is facing challenges from persistent selection bias and complex confounding.Bayesian Additive Regression Trees(BART)is em-ployed in this study to provide a flexible estimation of the academic perfor-mance.Targeted Maximum Likelihood Estimation(TMLE)is also integrated into the BART model,supporting doubly robust estimation of the special ed-ucation effect.This study extracted survey data from the Early Childhood Lon-gitudinal Study,Kindergarten Class(ECLS-K),to estimate the causal impact of special education status on students’combined mathematics and reading achievement scores.The analysis results of the BART-TMLE model show that children receiving special education services demonstrated approximately 9 points lower scores on average for combined math and reading scores,even adjusting for a considerable number of covariates,compared to their peers who did not receive these services.The estimated negative treatment effect persists after controlling for observed covariates that are closely correlated to the combined test score.The negative effect likely reflects unobserved factors,such as the underlying severity of learning disabilities,parent involvement and other potential traits,which are actual factors that determine the placement of special education status,rather than indicating the ineffectiveness of special education service.The achievement gap in academic performance reflects the current observable status of special education.The estimated effect could be improved by future research incorporating educational domain knowledge,allowing the model to be constructed more accurately.展开更多
Although amazing progress has been made in ma- chine learning to achieve high generalization accuracy and ef- ficiency, there is still very limited work on deriving meaning- ful decision-making actions from the result...Although amazing progress has been made in ma- chine learning to achieve high generalization accuracy and ef- ficiency, there is still very limited work on deriving meaning- ful decision-making actions from the resulting models. How- ever, in many applications such as advertisement, recommen- dation systems, social networks, customer relationship man- agement, and clinical prediction, the users need not only ac- curate prediction, but also suggestions on actions to achieve a desirable goal （e.g., high ads hit rates） or avert an unde- sirable predicted result （e.g., clinical deterioration）. Existing works for extracting such actionability are few and limited to simple models such as a decision tree. The dilemma is that those models with high accuracy are often more complex and harder to extract actionability from. In this paper, we propose an effective method to extract ac- tionable knowledge from additive tree models （ATMs）, one of the most widely used and best off-the-shelf classifiers. We rigorously formulate the optimal actionable planning （OAP） problem for a given ATM, which is to extract an action- able plan for a given input so that it can achieve a desirable output while maximizing the net profit. Based on a state space graph formulation, we first propose an optimal heuris- tic search method which intends to find an optimal solution. Then, we also present a sub-optimal heuristic search with an admissible and consistent heuristic function which can re- markably improve the efficiency of the algorithm. Our exper- imental results demonstrate the effectiveness and efficiency of the proposed algorithms on several real datasets in the application domain of personal credit and banking.展开更多
Bayesian Additive Regression Trees(BART)is a widely popular nonparametric regression model known for its accurate prediction capabilities.In certain situations,there is knowledge suggesting the existence of certain do...Bayesian Additive Regression Trees(BART)is a widely popular nonparametric regression model known for its accurate prediction capabilities.In certain situations,there is knowledge suggesting the existence of certain dominant variables.However,the BART model fails to fully utilize the knowledge.To tackle this problem,the paper introduces a modification to BART known as the Partially Fixed BART model.By fixing a portion of the trees’structure,this model enables more efficient utilization of prior knowledge,resulting in enhanced estimation accuracy.Moreover,the Partially Fixed BART model can offer more precise estimates and valuable insights for future analysis even when such prior knowledge is absent.Empirical results substantiate the enhancement of the proposed model in comparison to the original BART.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
文摘Determining the causal effect of special education is a critical topic when mak-ing educational policy that focuses on student achievement.However,current special education research is facing challenges from persistent selection bias and complex confounding.Bayesian Additive Regression Trees(BART)is em-ployed in this study to provide a flexible estimation of the academic perfor-mance.Targeted Maximum Likelihood Estimation(TMLE)is also integrated into the BART model,supporting doubly robust estimation of the special ed-ucation effect.This study extracted survey data from the Early Childhood Lon-gitudinal Study,Kindergarten Class(ECLS-K),to estimate the causal impact of special education status on students’combined mathematics and reading achievement scores.The analysis results of the BART-TMLE model show that children receiving special education services demonstrated approximately 9 points lower scores on average for combined math and reading scores,even adjusting for a considerable number of covariates,compared to their peers who did not receive these services.The estimated negative treatment effect persists after controlling for observed covariates that are closely correlated to the combined test score.The negative effect likely reflects unobserved factors,such as the underlying severity of learning disabilities,parent involvement and other potential traits,which are actual factors that determine the placement of special education status,rather than indicating the ineffectiveness of special education service.The achievement gap in academic performance reflects the current observable status of special education.The estimated effect could be improved by future research incorporating educational domain knowledge,allowing the model to be constructed more accurately.
基金This work was supported in part by China Postdoctoral Science Foundation （2013M531527）, the Fundamental Research Funds for the Central Universities （0110000037）, the National Natural Science Foun- dation of China （Grant Nos. 61502412, 61033009, and 61175057）, Natural Science Foundation of the Jiangsu Province （BK20150459）, Natural Science Foundation of the Jiangsu Higher Education Institutions （15KJB520036）, National Science Foundation, United States （IIS-0534699, IIS-0713109, CNS-1017701）, and a Microsoft Research New Faculty Fellowship.
文摘Although amazing progress has been made in ma- chine learning to achieve high generalization accuracy and ef- ficiency, there is still very limited work on deriving meaning- ful decision-making actions from the resulting models. How- ever, in many applications such as advertisement, recommen- dation systems, social networks, customer relationship man- agement, and clinical prediction, the users need not only ac- curate prediction, but also suggestions on actions to achieve a desirable goal （e.g., high ads hit rates） or avert an unde- sirable predicted result （e.g., clinical deterioration）. Existing works for extracting such actionability are few and limited to simple models such as a decision tree. The dilemma is that those models with high accuracy are often more complex and harder to extract actionability from. In this paper, we propose an effective method to extract ac- tionable knowledge from additive tree models （ATMs）, one of the most widely used and best off-the-shelf classifiers. We rigorously formulate the optimal actionable planning （OAP） problem for a given ATM, which is to extract an action- able plan for a given input so that it can achieve a desirable output while maximizing the net profit. Based on a state space graph formulation, we first propose an optimal heuris- tic search method which intends to find an optimal solution. Then, we also present a sub-optimal heuristic search with an admissible and consistent heuristic function which can re- markably improve the efficiency of the algorithm. Our exper- imental results demonstrate the effectiveness and efficiency of the proposed algorithms on several real datasets in the application domain of personal credit and banking.
文摘Bayesian Additive Regression Trees(BART)is a widely popular nonparametric regression model known for its accurate prediction capabilities.In certain situations,there is knowledge suggesting the existence of certain dominant variables.However,the BART model fails to fully utilize the knowledge.To tackle this problem,the paper introduces a modification to BART known as the Partially Fixed BART model.By fixing a portion of the trees’structure,this model enables more efficient utilization of prior knowledge,resulting in enhanced estimation accuracy.Moreover,the Partially Fixed BART model can offer more precise estimates and valuable insights for future analysis even when such prior knowledge is absent.Empirical results substantiate the enhancement of the proposed model in comparison to the original BART.
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.
