Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions
Abstract
Propensity score methods were proposed by Rosenbaum and Rubin (1983, Biometrika) as central tools to help assess the causal effects of interventions. Since their introduction two decades ago, they have found wide application in a variety of areas, including medical research, economics, epidemiology, and education, especially in those situations where randomized experiments are either difficult to perform, or raise ethical questions, or would require extensive delays before answers could be obtained. Rubin (1997, Annals of Internal Medicine) provides an introduction to some of the essential ideas. In the past few years, the number of published applications using propensity score methods to evaluate medical and epidemiological interventions has increased dramatically. Rubin (2003, Erlbaum) provides a summary, which is already out of date.
Cite
Text
Rubin. "Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions." European Conference on Machine Learning, 2003. doi:10.1007/978-3-540-39857-8_4Markdown
[Rubin. "Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions." European Conference on Machine Learning, 2003.](https://mlanthology.org/ecmlpkdd/2003/rubin2003ecml-taking/) doi:10.1007/978-3-540-39857-8_4BibTeX
@inproceedings{rubin2003ecml-taking,
title = {{Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions}},
author = {Rubin, Donald B.},
booktitle = {European Conference on Machine Learning},
year = {2003},
pages = {16-22},
doi = {10.1007/978-3-540-39857-8_4},
url = {https://mlanthology.org/ecmlpkdd/2003/rubin2003ecml-taking/}
}