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Heterogeneous treatment effect estimation is an essential element in the practice of tailoring treatment to suit the characteristics of individual patients. Most existing methods are not sufficiently robust against data irregularities. To enhance the robustness of the existing methods, we recently put forward a general estimating equation that unifies many existing learners. But the performance of model-based learners depends heavily on the correctness of the underlying treatment effect model. This paper addresses this vulnerability by converting the treatment effect estimation to a weighted supervised learning problem. We combine the general estimating equation with supervised learning algorithms, such as the gradient boosting machine, random forest, and artificial neural network, with appropriate modifications. This extension retains the estimators' robustness while enhancing their flexibility and scalability. Simulation shows that the algorithm-based estimation methods outperform their model-based counterparts in the presence of nonlinearity and non-additivity. We developed an R package, RCATE, for public access to the proposed methods. To illustrate the methods, we present a real data example to compare the blood pressure-lowering effects of two classes of antihypertensive agents. [ABSTRACT FROM AUTHOR]

Standard Shewhart &Xmacr; control charts with estimated control limits are widely used in practice. There are four ways to estimate the standard deviation σ generally, as which we call the average range, the average sample standard deviation, the pooled sample standard deviation and the average absolute deviation, respectively. We give three new estimators which are based on median in order to estimate standard deviation in this paper, and we get their means through simulated computation. The simulated results are then used to discuss their properties when the data is from a ε-contaminated normal distribution. At last, we simulate the in-control and outof-control average run lengths of Shewhart &Xmacr; control charts when the process standard deviation is estimated by seven different ways including three absolute deviations to median mentioned in the paper. [ABSTRACT FROM AUTHOR]