3 results about "Outcome variable" patented technology

The disclosure provides a multi-agent data joint method, system, server and terminal device, original data is distributed in K data owners, the original data includes instrumental variables, exposure factors and outcome variables, the method comprises the following steps: the K data owners encrypt the instrumental variables and the exposure factors by using a first encryption key; receiving the encrypted instrumental variables and the exposure factors and obtaining a first regression coefficient; the K data owners obtain the predicted value of the exposure factors by using the first regression coefficient and the instrumental variables, and encrypt the predicted value and the outcome variables by using a second encryption key; receiving the encrypted predicted value and the outcome variables and obtaining a second regression coefficient; the K data owners decrypt the second regression coefficient to obtain the regression coefficient between the predicted value of the exposure factors and the outcome variables in the original data. Thus, under the premise of ensuring data privacy, cross-institutional sharing and data fusion are realized, and Mendelian randomization analysis is completed.

A method and system for analyzing urban green development efficiency, belonging to the field of smart city construction, is presented. It addresses the limitations of existing green productivity measurement methods in handling high-dimensional control variables and potential nonlinear relationships, as well as the inaccurate identification of causal effects. The method includes: acquiring a panel dataset of the target city; calculating a green efficiency value, stripped of environmental factors and random noise, using a data envelopment analysis model adjusted by stochastic frontier analysis; constructing and executing a dual machine learning model using the green efficiency value as the outcome variable, a pre-defined treatment variable as the treatment variable, and a high-dimensional control variable as a covariate; obtaining an estimate of the treatment effect of the treatment variable on the outcome variable through cross-fitting and residual regression; and generating analytical results on urban green development efficiency based on the green efficiency value and the estimated treatment effect. It is primarily used in the field of green productivity measurement.

This invention belongs to the field of educational data analysis and machine learning technology, specifically disclosing a method for analyzing performance influencing factors and causal effects that integrates Direct Machine Learning (DML) and Conformal Prediction (CP). This method introduces the orthogonalized causal estimation mechanism of dual machine learning into the analysis of performance influencing factors, and combines it with the interval construction idea of ​​conformal prediction to achieve the identification of performance influencing factors and the evaluation of causal effect intervals. By constructing outcome variable models and treatment variable models, the influence of high-dimensional covariates is eliminated through residualization, and orthogonal scores are used to achieve robust estimation of the causal effects of candidate factors on performance variables. Furthermore, the orthogonal scores are used as a measure of inconsistency in conformal prediction to determine the acceptance region of candidate causal effect parameters, thereby generating causal effect intervals. This method has good interpretability and practicality in identifying performance influencing factors, estimating causal effects, and quantifying intervals.