Sufficient covariates and linear propensity analysis

Hui Guo, Philip Dawid
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:281-288, 2010.

Abstract

Working within the decision-theoretic framework for causal inference, we study the properties of “sufficient covariates", which support causal inference from observational data, and possibilities for their reduction. In particular we illustrate the role of a propensity variable by means of a simple model, and explain why such a reduction typically does not increase (and may reduce) estimation efficiency.

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-guo10a, title = {Sufficient covariates and linear propensity analysis}, author = {Guo, Hui and Dawid, Philip}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {281--288}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/guo10a/guo10a.pdf}, url = {https://proceedings.mlr.press/v9/guo10a.html}, abstract = {Working within the decision-theoretic framework for causal inference, we study the properties of “sufficient covariates", which support causal inference from observational data, and possibilities for their reduction. In particular we illustrate the role of a propensity variable by means of a simple model, and explain why such a reduction typically does not increase (and may reduce) estimation efficiency.} }
Endnote
%0 Conference Paper %T Sufficient covariates and linear propensity analysis %A Hui Guo %A Philip Dawid %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-guo10a %I PMLR %P 281--288 %U https://proceedings.mlr.press/v9/guo10a.html %V 9 %X Working within the decision-theoretic framework for causal inference, we study the properties of “sufficient covariates", which support causal inference from observational data, and possibilities for their reduction. In particular we illustrate the role of a propensity variable by means of a simple model, and explain why such a reduction typically does not increase (and may reduce) estimation efficiency.
RIS
TY - CPAPER TI - Sufficient covariates and linear propensity analysis AU - Hui Guo AU - Philip Dawid BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-guo10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 281 EP - 288 L1 - http://proceedings.mlr.press/v9/guo10a/guo10a.pdf UR - https://proceedings.mlr.press/v9/guo10a.html AB - Working within the decision-theoretic framework for causal inference, we study the properties of “sufficient covariates", which support causal inference from observational data, and possibilities for their reduction. In particular we illustrate the role of a propensity variable by means of a simple model, and explain why such a reduction typically does not increase (and may reduce) estimation efficiency. ER -
APA
Guo, H. & Dawid, P.. (2010). Sufficient covariates and linear propensity analysis. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:281-288 Available from https://proceedings.mlr.press/v9/guo10a.html.

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