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There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is muchmore » larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.« less

Here, three types of wood pellets samples, including two types of commercial pellets and one type of lab-made control pellets were torrefied in a fixed bed unit to study the effect of thermal pretreatment on the quality of wood pellets. The quality of wood pellets was mainly characterized by the pellet density, bulk density, higher heating value, Meyer hardness, saturated moisture uptake, volumetric energy density, and energy yield. Results showed that torrefaction significantly decreased the pellet density, hardness, volumetric energy density, and energy yield. The higher heating value increased and the saturated moisture content decreased after torrefaction. In view ofmore » the lower density, lower hardness, lower volumetric energy density, and energy yield of torrefied pellets, it is recommended that biomass should be torrefied and then compressed to make strong pellets of high hydrophobicity and volumetric energy density.« less