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  "Title": "Heterogeneous Effects Analysis of Conjoint Experiments",
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  "Description": "A tool for analyzing conjoint experiments using Bayesian\nAdditive Regression Trees ('BART'), a machine learning method\ndeveloped by Chipman, George and McCulloch (2010)\n<doi:10.1214/09-AOAS285>. This tool focuses specifically on\nestimating, identifying, and visualizing the heterogeneity\nwithin marginal component effects, at the observation- and\nindividual-level. It uses a variable importance measure\n('VIMP') with delete-d jackknife variance estimation, following\nIshwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain\nbias-corrected estimates of which variables drive heterogeneity\nin the predicted individual-level effects.",
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