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personalized2part: Two-Part Estimation of Treatment Rules for Semi-Continuous Data
Implements the methodology of Huling, Smith, and
Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification
for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part
modeling framework to handle semi-continuous data by separately modeling the positive part
of the outcome and an indicator of whether each outcome is positive, but still results in a
single treatment rule. High dimensional data is handled with a cooperative lasso penalty,
which encourages the coefficients in the two models to have the same sign.
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