Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders

Sorawit Saengkyongam, Ricardo Silva
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:300-309, 2020.

Abstract

We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders. To overcome the problem of hidden confounding, we consider the setting where we have access to not only the observational data but also sets of single-variable interventions in which each of the treatment variables is intervened on separately. We prove identifiability under the assumption that the data is generated from a nonlinear continuous structural causal model with additive Gaussian noise. In addition, we propose a simple parameter estimation method by pooling all the data from different regimes and jointly maximizing the combined likelihood. We also conduct comprehensive experiments to verify the identifiability result as well as to compare the performance of our approach against a baseline on both synthetic and real-world data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v124-saengkyongam20a, title = {Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders}, author = {Saengkyongam, Sorawit and Silva, Ricardo}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {300--309}, year = {2020}, editor = {Peters, Jonas and Sontag, David}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/saengkyongam20a/saengkyongam20a.pdf}, url = {https://proceedings.mlr.press/v124/saengkyongam20a.html}, abstract = {We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders. To overcome the problem of hidden confounding, we consider the setting where we have access to not only the observational data but also sets of single-variable interventions in which each of the treatment variables is intervened on separately. We prove identifiability under the assumption that the data is generated from a nonlinear continuous structural causal model with additive Gaussian noise. In addition, we propose a simple parameter estimation method by pooling all the data from different regimes and jointly maximizing the combined likelihood. We also conduct comprehensive experiments to verify the identifiability result as well as to compare the performance of our approach against a baseline on both synthetic and real-world data.} }
Endnote
%0 Conference Paper %T Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders %A Sorawit Saengkyongam %A Ricardo Silva %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-saengkyongam20a %I PMLR %P 300--309 %U https://proceedings.mlr.press/v124/saengkyongam20a.html %V 124 %X We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders. To overcome the problem of hidden confounding, we consider the setting where we have access to not only the observational data but also sets of single-variable interventions in which each of the treatment variables is intervened on separately. We prove identifiability under the assumption that the data is generated from a nonlinear continuous structural causal model with additive Gaussian noise. In addition, we propose a simple parameter estimation method by pooling all the data from different regimes and jointly maximizing the combined likelihood. We also conduct comprehensive experiments to verify the identifiability result as well as to compare the performance of our approach against a baseline on both synthetic and real-world data.
APA
Saengkyongam, S. & Silva, R.. (2020). Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:300-309 Available from https://proceedings.mlr.press/v124/saengkyongam20a.html.

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