2. DoubleML Models

DoubleMLPLR(obj_dml_data, ml_l, ml_m[, ...])	Double machine learning for partially linear regression models
DoubleMLPLIV(obj_dml_data, ml_l, ml_m, ml_r)	Double machine learning for partially linear IV regression models
DoubleMLIRM(obj_dml_data, ml_g, ml_m[, ...])	Double machine learning for interactive regression models
DoubleMLAPO(obj_dml_data, ml_g, ml_m, ...[, ...])	Double machine learning average potential outcomes for interactive regression models.
DoubleMLAPOS(obj_dml_data, ml_g, ml_m, ...)	Double machine learning for interactive regression models with multiple discrete treatments.
DoubleMLIIVM(obj_dml_data, ml_g, ml_m, ml_r)	Double machine learning for interactive IV regression models
DoubleMLDID(obj_dml_data, ml_g[, ml_m, ...])	Double machine learning for difference-in-differences models with panel data (two time periods).
DoubleMLDIDCS(obj_dml_data, ml_g[, ml_m, ...])	Double machine learning for difference-in-difference with repeated cross-sections.
DoubleMLSSM(obj_dml_data, ml_g, ml_pi, ml_m)	Double machine learning for sample selection models
DoubleMLPQ(obj_dml_data, ml_g, ml_m[, ...])	Double machine learning for potential quantiles
DoubleMLLPQ(obj_dml_data, ml_g, ml_m[, ...])	Double machine learning for local potential quantiles
DoubleMLCVAR(obj_dml_data, ml_g, ml_m[, ...])	Double machine learning for conditional value at risk for potential outcomes
DoubleMLQTE(obj_dml_data, ml_g[, ml_m, ...])	Double machine learning for quantile treatment effects