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  "Title": "Double/Debiased Machine Learning",
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  "Description": "Estimate common causal parameters using double/debiased\nmachine learning as proposed by Chernozhukov et al. (2018)\n<doi:10.1111/ectj.12097>. 'ddml' simplifies estimation based on\n(short-)stacking as discussed in Ahrens et al. (2024)\n<doi:10.1002/jae.3103>, which leverages multiple base learners\nto increase robustness to the underlying data generating\nprocess.",
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    "mdl_glmnet",
    "mdl_ranger",
    "mdl_xgboost",
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    "tidy"
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        "array"
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      "tojson": true
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      "topics": [
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      ]
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      "page": "as.list.ddml",
      "title": "Split a DDML Object by Ensemble Type",
      "topics": [
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      ]
    },
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      "page": "as.list.ddml_rep",
      "title": "Split a ddml_rep Object by Ensemble Type",
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      "page": "coef.ral",
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      "topics": [
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      ]
    },
    {
      "page": "confint.ral_rep",
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    {
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    },
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      "title": "Construct a 'ddml' Object.",
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      ],
      "topics": [
        "ddml"
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    },
    {
      "page": "ddml_apo",
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      "concept": [
        "ddml estimators"
      ],
      "topics": [
        "ddml_apo"
      ]
    },
    {
      "page": "ddml_ate",
      "title": "Estimator for the Average Treatment Effect",
      "concept": [
        "ddml estimators"
      ],
      "topics": [
        "ddml_ate",
        "ddml_att"
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    {
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      "title": "Estimator for Group-Time Average Treatment Effects",
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        "ddml estimators"
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      "topics": [
        "ddml_attgt"
      ]
    },
    {
      "page": "ddml_fpliv",
      "title": "Estimator for the Flexible Partially Linear IV Coefficient",
      "concept": [
        "ddml estimators"
      ],
      "topics": [
        "ddml_fpliv"
      ]
    },
    {
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      "title": "Estimator for the Local Average Treatment Effect",
      "concept": [
        "ddml estimators"
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      "topics": [
        "ddml_late"
      ]
    },
    {
      "page": "ddml_pliv",
      "title": "Estimator for the Partially Linear IV Coefficient",
      "concept": [
        "ddml estimators"
      ],
      "topics": [
        "ddml_pliv"
      ]
    },
    {
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      "title": "Estimator for the Partially Linear Regression Coefficient",
      "concept": [
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      ],
      "topics": [
        "ddml_plm"
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    },
    {
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      "concept": [
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      "topics": [
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      ]
    },
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      "title": "Construct a Multi-Resample DDML Object",
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        "print.ddml_rep"
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        "ddml_replicate"
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      "concept": [
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        "ddml-intro"
      ]
    },
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      "title": "Stacking Diagnostics for DDML Estimators",
      "concept": [
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    },
    {
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      "concept": [
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      "topics": [
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    },
    {
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    {
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      "title": "Glance at a DDML Object",
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        "glance.ddml"
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    },
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      "title": "Glance at a ddml_rep Object",
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    },
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      "title": "Glance at a RAL Object",
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      ]
    },
    {
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      "title": "Glance at a RAL Rep Object",
      "topics": [
        "glance.ral_rep"
      ]
    },
    {
      "page": "hatvalues.ral",
      "title": "Extract leverage (Hat Values)",
      "topics": [
        "hatvalues.ral"
      ]
    },
    {
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      "title": "Linear Combinations of DDML Coefficients",
      "concept": [
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      ],
      "topics": [
        "lincom",
        "lincom.ddml",
        "lincom.ddml_rep",
        "print.lincom",
        "print.lincom_rep"
      ]
    },
    {
      "page": "lincom_weights_did",
      "title": "Difference-in-Differences Aggregation Weights for lincom",
      "topics": [
        "lincom_weights_did"
      ]
    },
    {
      "page": "mdl_bigGLM",
      "title": "Wrapper for glmnet::bigGlm()",
      "concept": [
        "ml_wrapper"
      ],
      "topics": [
        "mdl_bigGlm"
      ]
    },
    {
      "page": "mdl_glm",
      "title": "Wrapper for stats::glm()",
      "concept": [
        "ml_wrapper"
      ],
      "topics": [
        "mdl_glm"
      ]
    },
    {
      "page": "mdl_glmnet",
      "title": "Wrapper for glmnet::glmnet()",
      "concept": [
        "ml_wrapper"
      ],
      "topics": [
        "mdl_glmnet"
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    },
    {
      "page": "mdl_ranger",
      "title": "Wrapper for ranger::ranger()",
      "concept": [
        "ml_wrapper"
      ],
      "topics": [
        "mdl_ranger"
      ]
    },
    {
      "page": "mdl_xgboost",
      "title": "Wrapper for xgboost::xgboost()",
      "concept": [
        "ml_wrapper"
      ],
      "topics": [
        "mdl_xgboost"
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    {
      "page": "nobs.ral",
      "title": "Number of Observations in a RAL Object",
      "topics": [
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      ]
    },
    {
      "page": "ols",
      "title": "Ordinary Least Squares",
      "concept": [
        "ml_wrapper"
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        "ols"
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    },
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      "page": "plot.ral",
      "title": "Plot Coefficients from a RAL Estimator",
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        "plot.ral_rep"
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    },
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      "page": "predict.ensemble",
      "title": "Predict Method for 'ensemble' Objects",
      "topics": [
        "predict.ensemble"
      ]
    },
    {
      "page": "predict.mdl_bigGLM",
      "title": "Predict Method for mdl_bigGlm Objects",
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        "predict.mdl_bigGlm"
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    },
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      "page": "predict.mdl_glm",
      "title": "Predict Method for mdl_glm Objects",
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    },
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      "page": "predict.mdl_glmnet",
      "title": "Predict Method for mdl_glmnet Objects",
      "topics": [
        "predict.mdl_glmnet"
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      "page": "predict.mdl_ranger",
      "title": "Predict Method for mdl_ranger Objects",
      "topics": [
        "predict.mdl_ranger"
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    },
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      "page": "predict.mdl_xgboost",
      "title": "Predict Method for mdl_xgboost Objects",
      "topics": [
        "predict.mdl_xgboost"
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    {
      "page": "predict.ols",
      "title": "Predict Method for ols Objects",
      "topics": [
        "predict.ols"
      ]
    },
    {
      "page": "print.ddml_diagnostics",
      "title": "Print Stacking Diagnostics",
      "topics": [
        "print.ddml_diagnostics"
      ]
    },
    {
      "page": "ral",
      "title": "Construct a RAL Inference Object",
      "topics": [
        "ral"
      ]
    },
    {
      "page": "ral_rep",
      "title": "Construct a Replicated RAL Inference Object",
      "topics": [
        "ral_rep"
      ]
    },
    {
      "page": "shortstacking",
      "title": "Predictions using Short-Stacking",
      "concept": [
        "utilities"
      ],
      "topics": [
        "shortstacking"
      ]
    },
    {
      "page": "summary.ddml",
      "title": "Summary for DDML Estimators",
      "concept": [
        "ddml inference"
      ],
      "topics": [
        "print.summary.ddml",
        "summary.ddml"
      ]
    },
    {
      "page": "summary.ddml_rep",
      "title": "Summary for ddml_rep Objects",
      "topics": [
        "print.summary.ddml_rep",
        "summary.ddml_rep"
      ]
    },
    {
      "page": "summary.ral",
      "title": "Summary for RAL Estimators",
      "topics": [
        "print.summary.ral",
        "summary.ral"
      ]
    },
    {
      "page": "summary.ral_rep",
      "title": "Summary for RAL Rep Objects",
      "topics": [
        "print.summary.ral_rep",
        "summary.ral_rep"
      ]
    },
    {
      "page": "tidy.ddml",
      "title": "Tidy a DDML Object",
      "topics": [
        "tidy.ddml"
      ]
    },
    {
      "page": "tidy.ddml_diagnostics",
      "title": "Tidy Stacking Diagnostics",
      "topics": [
        "tidy.ddml_diagnostics"
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    },
    {
      "page": "tidy.ddml_rep",
      "title": "Tidy a ddml_rep Object",
      "topics": [
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    {
      "page": "tidy.ral",
      "title": "Tidy a RAL Object",
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      "title": "Tidy a RAL Rep Object",
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      "page": "vcov.ral",
      "title": "Variance-Covariance Matrix for RAL Estimators",
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        "vcov.ral"
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    },
    {
      "page": "vcov.ral_rep",
      "title": "Variance-Covariance Matrix for RAL Rep Objects",
      "topics": [
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