All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples

Rudolph, K. E., Williams, N. T., Miles, C. H., Antonelli, J., & Diaz, I. (2023). All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples. Journal of Causal Inference, 11(1). https://doi.org/10.1515/jci-2023-0022
Authors:
Kara E. Rudolph
Nicholas T. Williams
Caleb H. Miles
Joseph Antonelli
Ivan Diaz
Affiliated Authors:
Nicholas T. Williams
Caleb H. Miles
Columbia Affiliation:
Author Keywords:
causal inference
nonparametric
parametric
elink
Publication Type:
Article
Unique ID:
10.1515/jci-2023-0022
DOI:
10.1515/jci-2023-0022
Journal:
Journal of Causal Inference
Publication Date:
Data Source:
Scopus
Source Link:
View Scopus Record

Record Created: