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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. Achieves double\nrobustness: consistent estimation when either the propensity\nscore or prognostic score model is correctly specified.\nProvides polynomial sieve bias correction and\nlinearization-based multinomial bootstrap variance estimation\nthat preserves the survey-weighted matching structure without\nre-matching. Methods are described in Zeng, Tong, Tong, Lu,\nMukherjee, and Li (2026, under review) \"Where to weight?\nEstimating population causal effects with weighted double score\nmatching in complex surveys\".",
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