\[ \usepackage{dsfont} \usepackage{xcolor} \require{mathtools} \definecolor{bayesred}{RGB}{147, 30, 24} \definecolor{bayesblue}{RGB}{32, 35, 91} \definecolor{bayesorange}{RGB}{218, 120, 1} \definecolor{grey}{RGB}{128, 128, 128} \definecolor{couleur1}{RGB}{0,163,137} \definecolor{couleur2}{RGB}{255,124,0} \definecolor{couleur3}{RGB}{0, 110, 158} \definecolor{coul1}{RGB}{255,37,0} \definecolor{coul2}{RGB}{242,173,0} \definecolor{col_neg}{RGB}{155, 191, 221} \definecolor{col_pos}{RGB}{255, 128, 106} {\color{bayesorange} P (\text{H} \mid \text{E})} = \frac {{\color{bayesred} P(\text{H})} \times {\color{bayesblue}P(\text{E} \mid \text{H})}} {\color{grey} {P(\text{E})}} \]
This online appendix provides R codes to apply the methods presented in Charpentier, Flachaire, and Gallic (2023).
Univariate case:
Multivariate case:
The working paper is available on https://arxiv.org/abs/2301.07755.
Charpentier, A., Flachaire, E. & Gallic, E. (2023). Optimal Transport for Counterfactual Estimation: A Method for Causal Inference . arXiv:2301.07755.doi arXiv
bib entry
@misc{https://doi.org/10.48550/arxiv.2301.07755,
doi = {10.48550/ARXIV.2301.07755},
url = {https://arxiv.org/abs/2301.07755},
author = {Charpentier, Arthur and Flachaire, Emmanuel and Gallic, Ewen},
keywords = {Econometrics (econ.EM), FOS: Economics and business, FOS: Economics and business},
title = {Optimal Transport for Counterfactual Estimation: A Method for Causal Inference},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution 4.0 International}
}