overlapping

Function to perform overlap analysis between train and test datasets.
The function performs principal component analysis (PCA) on the covariates for both sets
and calculates the Kolmogorov-Smirnov test for overlap.

Provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based
on data in order to maximize empirical welfare. Specifically, 'OPL' allows to find "treatment assignment rules" that maximize the overall
welfare, defined as the sum of the policy effects estimated over all the policy beneficiaries. Documentation about 'OPL' is provided by
several international articles via Athey et al (2021, <doi:10.3982/ECTA15732>), Kitagawa et al (2018, <doi:10.3982/ECTA13288>),
Cerulli (2022, <doi:10.1080/13504851.2022.2032577>), the paper by Cerulli (2021, <doi:10.1080/13504851.2020.1820939>)
and the book by Gareth et al (2013, <doi:10.1007/978-1-4614-7138-7>).

Federico Brogi

Optimal Policy Learning

Barbara Guardabascio

Giovanni Cerulli

overlapping function

<dl><dt>train_data</dt>
<dd>Train Dataset indicating the old policy sample.</dd>
<dt>test_data</dt>
<dd>Test Dataset indicating the new policy sample.</dd>
<dt>x</dt>
<dd>Vector of predictor variables.</dd></dl>

Arguments

Testing overlap between old and new policy sample — overlapping

<dl>

<dt>train_data</dt>
<dd>Train Dataset indicating the old policy sample.</dd>


<dt>test_data</dt>
<dd>Test Dataset indicating the new policy sample.</dd>


<dt>x</dt>
<dd>Vector of predictor variables.</dd>

</dl>

overlapping: Testing overlap between old and new policy sample

Description

Usage

Value

Arguments