Likelihood-Based Causal Inference
Abstract
A method is given which uses subject matter assumptions to discriminate recursive models and thus point toward possible causal explanations. The assumptions alone do not specify any order among the variables - rather just a theoretical absence of direct association. We show how these assumptions, while not specifying any ordering, can when combined with the data through the likelihood function yield information about an underlying recursive order. We derive details of the method for multinormal random variables and apply the procedure to a simulated example.
Cite
Text
Yao and Tritchler. "Likelihood-Based Causal Inference." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[Yao and Tritchler. "Likelihood-Based Causal Inference." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/yao1995aistats-likelihoodbased/)BibTeX
@inproceedings{yao1995aistats-likelihoodbased,
title = {{Likelihood-Based Causal Inference}},
author = {Yao, Qing and Tritchler, David},
booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
year = {1995},
pages = {520-530},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/yao1995aistats-likelihoodbased/}
}