Inference of causal effects when control variables are unknown

Ludvig Hult, Dave Zachariah
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1300-1309, 2021.

Abstract

Conventional methods in causal effect inference typically rely on specifying a valid set of control variables. When this set is unknown or misspecified, inferences will be erroneous. We propose a method for inferring average causal effects when all potential confounders are observed, but the control variables are unknown. When the data-generating process belongs to the class of acyclical linear structural causal models, we prove that the method yields asymptotically valid confidence intervals. Our results build upon a smooth characterization of linear directed acyclic graphs. We verify the capability of the method to produce valid confidence intervals for average causal effects using synthetic data, even when the appropriate specification of control variables is unknown.

Cite this Paper

BibTeX
@InProceedings{pmlr-v161-hult21a, title = {Inference of causal effects when control variables are unknown}, author = {Hult, Ludvig and Zachariah, Dave}, booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence}, pages = {1300--1309}, year = {2021}, editor = {de Campos, Cassio and Maathuis, Marloes H.}, volume = {161}, series = {Proceedings of Machine Learning Research}, month = {27--30 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v161/hult21a/hult21a.pdf}, url = {https://proceedings.mlr.press/v161/hult21a.html}, abstract = {Conventional methods in causal effect inference typically rely on specifying a valid set of control variables. When this set is unknown or misspecified, inferences will be erroneous. We propose a method for inferring average causal effects when all potential confounders are observed, but the control variables are unknown. When the data-generating process belongs to the class of acyclical linear structural causal models, we prove that the method yields asymptotically valid confidence intervals. Our results build upon a smooth characterization of linear directed acyclic graphs. We verify the capability of the method to produce valid confidence intervals for average causal effects using synthetic data, even when the appropriate specification of control variables is unknown.} }
Endnote
%0 Conference Paper %T Inference of causal effects when control variables are unknown %A Ludvig Hult %A Dave Zachariah %B Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2021 %E Cassio de Campos %E Marloes H. Maathuis %F pmlr-v161-hult21a %I PMLR %P 1300--1309 %U https://proceedings.mlr.press/v161/hult21a.html %V 161 %X Conventional methods in causal effect inference typically rely on specifying a valid set of control variables. When this set is unknown or misspecified, inferences will be erroneous. We propose a method for inferring average causal effects when all potential confounders are observed, but the control variables are unknown. When the data-generating process belongs to the class of acyclical linear structural causal models, we prove that the method yields asymptotically valid confidence intervals. Our results build upon a smooth characterization of linear directed acyclic graphs. We verify the capability of the method to produce valid confidence intervals for average causal effects using synthetic data, even when the appropriate specification of control variables is unknown.
APA
Hult, L. & Zachariah, D.. (2021). Inference of causal effects when control variables are unknown. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 161:1300-1309 Available from https://proceedings.mlr.press/v161/hult21a.html.

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