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  "Title": "Distributional Synthetic Controls Estimation",
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  "Authors@R": "c(person(\"David\", \"Van Dijcke\", , \"dvdijcke@umich.edu\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-5825-3447\")),\nperson(\"Florian\", \"Gunsilius\", , \"ffg@umich.edu\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-1698-6324\")),\nperson(\"Siyun\", \"He\", , \"siyunhe@umich.edu\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-4201-0912\"))\n)",
  "Description": "The method of synthetic controls is a widely-adopted tool\nfor evaluating causal effects of policy changes in settings\nwith observational data. In many settings where it is\napplicable, researchers want to identify causal effects of\npolicy changes on a treated unit at an aggregate level while\nhaving access to data at a finer granularity. This package\nimplements a simple extension of the synthetic controls\nestimator, developed in Gunsilius (2023)\n<doi:10.3982/ECTA18260>, that takes advantage of this\nadditional structure and provides nonparametric estimates of\nthe heterogeneity within the aggregate unit. The idea is to\nreplicate the quantile function associated with the treated\nunit by a weighted average of quantile functions of the control\nunits. The package contains tools for aggregating and plotting\nthe resulting distributional estimates, as well as for carrying\nout inference on them.",
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