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We deal with a core problem in causal inference: estimating heterogeneous procedure outcomes using panel facts with typical remedy patterns. Lots of existing solutions either tend not to use the potential fundamental construction in panel details or have limitations during the allowable cure styles. With this perform, we suggest and Examine a new process that initial partitions observations into disjoint clusters with equivalent treatment method effects using a regression tree, after which you can leverages the (assumed) minimal-rank construction from the panel facts to estimate the common cure outcome for each cluster.
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