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  "Title": "Spectrally Deconfounded Models",
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  "Authors@R": "c(\nperson(\"Markus\", \"Ulmer\", email = \"markus.ulmer@stat.math.ethz.ch\",\nrole = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0001-7783-8475\")),\nperson(\"Cyrill\", \"Scheidegger\", role = \"aut\", comment = c(ORCID = \"0009-0005-2851-1384\")))",
  "Description": "Screen for and analyze non-linear sparse direct effects in\nthe presence of unobserved confounding using the spectral\ndeconfounding techniques (Ćevid, Bühlmann, and Meinshausen\n(2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ćevid, and\nBühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have\nbeen shown to be a good estimate for the true direct effect if\nwe observe many covariates, e.g., high-dimensional settings,\nand we have fairly dense confounding. Even if the assumptions\nare violated, it seems like there is not much to lose, and the\ndeconfounded models will, in general, estimate a function\ncloser to the true one than classical least squares\noptimization. 'SDModels' provides functions SDAM() for\nSpectrally Deconfounded Additive Models (Scheidegger, Guo, and\nBühlmann (2025) <doi:10.1145/3711116>) and SDForest() for\nSpectrally Deconfounded Random Forests (Ulmer, Scheidegger, and\nBühlmann (2025) <doi:10.1080/10618600.2025.2569602>).",
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  "Date/Publication": "2025-12-12 16:40:09 UTC",
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      "title": "Plot partial dependence",
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    },
    {
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      "title": "Visualize the paths of an SDTree or SDForest",
      "topics": [
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      ]
    },
    {
      "page": "plot.SDForest",
      "title": "Plot performance of SDForest against number of trees",
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    },
    {
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      "title": "Plot SDTree",
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      "page": "plotOOB",
      "title": "Visualize the out-of-bag performance of an SDForest",
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      "title": "Predictions of individual component functions for SDAM",
      "topics": [
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      "title": "Predictions for SDAM",
      "topics": [
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      "title": "Predictions for the SDTree",
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      "title": "Spectrally Deconfounded Additive Models",
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