5 Simple Statements About Stanford Case Study Solution Explained
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We address a Main issue in causal inference: estimating heterogeneous remedy consequences utilizing panel info with normal remedy designs. Quite a few current approaches both never use the likely underlying framework in panel data or have restrictions during the allowable procedure patterns.
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In this get the job done, we propose and Assess a whole new technique that very first partitions observations into disjoint clusters with identical treatment outcomes using a regression tree, and then leverages the (assumed) small-rank construction with the panel details to estimate the average remedy influence for each cluster.
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