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  "Title": "Matching Methods for Causal Inference with Time-Series\nCross-Sectional Data",
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  "Date": "2020-02-10",
  "Authors@R": "c(\nperson(\"In Song\", \"Kim\", email = \"insong@mit.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Adam\", \"Rauh\", email = \"amrauh@umich.edu\", role = c(\"aut\")),\nperson(\"Erik\", \"Wang\", email = \"haixiaow@Princeton.edu\", role = c(\"aut\")),\nperson(\"Kosuke\", \"Imai\", email = \"imai@harvard.edu\", role = c(\"aut\"))\n)",
  "Description": "Implements a set of methodological tools that enable\nresearchers to apply matching methods to time-series\ncross-sectional data. Imai, Kim, and Wang (2018)\n<http://web.mit.edu/insong/www/pdf/tscs.pdf> proposes a\nnonparametric generalization of the difference-in-differences\nestimator, which does not rely on the linearity assumption as\noften done in practice. Researchers first select a method of\nmatching each treated observation for a given unit in a\nparticular time period with control observations from other\nunits in the same time period that have a similar treatment and\ncovariate history. These methods include standard matching\nmethods based on propensity score and Mahalanobis distance, as\nwell as weighting methods. Once matching is done, both\nshort-term and long-term average treatment effects for the\ntreated can be estimated with standard errors. The package also\noffers a visualization technique that allows researchers to\nassess the quality of matches by examining the resulting\ncovariate balance.",
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