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  "Title": "Weighted Double Score Matching for Survey-Weighted Causal\nInference",
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  "Authors@R": "c(\nperson(\"Yukang\", \"Zeng\", email = \"ykzeng2019@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Guangyu\", \"Tong\", role = \"aut\"),\nperson(\"Jiaqi\", \"Tong\", role = \"aut\"),\nperson(\"Haidong\", \"Lu\", role = \"aut\"),\nperson(\"Bhramar\", \"Mukherjee\", role = \"aut\"),\nperson(\"Fan\", \"Li\", email = \"fan.f.li@yale.edu\", role = \"aut\"))",
  "Description": "Implements weighted double score matching (WDSM) for\nestimating population-level causal effects from complex survey\ndata. Combines propensity scores and prognostic scores with\nsurvey design weights for matching, survey-weighted imputation\nwithin match sets, and Hajek normalization to target the\npopulation average treatment effect (PATE) and the population\naverage treatment effect on the treated (PATT). Supports both\nretrospective (treatment-dependent) and prospective\n(treatment-independent) sampling designs. Uses propensity\nprobabilities and arm-specific prognostic scores for matching,\nwith a complete quadratic bias correction in each arm's double\nscore. Provides linearization-based multinomial replication\nvariance estimates and centered normal Wald confidence\nintervals, retaining the original matching reuse coefficients\nwithout re-matching. Supplied scores can be held fixed for\ninference conditional on those scores. This weight-only\ninterface does not encode survey strata, clusters, or\ndesign-specific replicate weights. The method is described by\nZeng et al. (2026) <doi:10.48550/arXiv.2610.25870>.",
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