Likelihood-based Causal Inference

Qing Yao, David Tritchler
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:520-530, 1995.

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 this Paper

BibTeX
@InProceedings{pmlr-vR0-yao95a, title = {Likelihood-based Causal Inference}, author = {Yao, Qing and Tritchler, David}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {520--530}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/yao95a/yao95a.pdf}, url = {https://proceedings.mlr.press/r0/yao95a.html}, 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.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Likelihood-based Causal Inference %A Qing Yao %A David Tritchler %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-yao95a %I PMLR %P 520--530 %U https://proceedings.mlr.press/r0/yao95a.html %V R0 %X 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. %Z Reissued by PMLR on 01 May 2022.
APA
Yao, Q. & Tritchler, D.. (1995). Likelihood-based Causal Inference. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:520-530 Available from https://proceedings.mlr.press/r0/yao95a.html. Reissued by PMLR on 01 May 2022.

Related Material