Causal Learning and Machine Learning

Kun Zhang
Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:4-4, 2017.

Abstract

Can we find the causal direction between two variables? How can we make optimal predictions in the presence of distribution shift? We are often faced with such causal modeling or prediction problems. Recently, with the rapid accumulation of huge volumes of data, both causal discovery, i.e., learning causal information from purely observational data, and machine learning are seeing exciting opportunities as well as great challenges. This talk will be focused on recent advances in causal discovery and how causal information facilitates understanding and solving certain problems of learning from heterogeneous data. In particular, I will talk about basic approaches to causal discovery and address practical issues in causal discovery, including nonstationarity or heterogeneity of the data and existence of measurement error. Finally, I will discuss why and how underlying causal knowledge helps in learning from heterogeneous data when the i.i.d. assumption is dropped, with transfer learning? as a particular example.

Cite this Paper

BibTeX
@InProceedings{pmlr-v73-zhang17a, title = {Causal Learning and Machine Learning}, author = {Zhang, Kun}, booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks}, pages = {4--4}, year = {2017}, editor = {Hyttinen, Antti and Suzuki, Joe and Malone, Brandon}, volume = {73}, series = {Proceedings of Machine Learning Research}, month = {20--22 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v73/zhang17a/zhang17a.pdf}, url = {https://proceedings.mlr.press/v73/zhang17a.html}, abstract = {Can we find the causal direction between two variables? How can we make optimal predictions in the presence of distribution shift? We are often faced with such causal modeling or prediction problems. Recently, with the rapid accumulation of huge volumes of data, both causal discovery, i.e., learning causal information from purely observational data, and machine learning are seeing exciting opportunities as well as great challenges. This talk will be focused on recent advances in causal discovery and how causal information facilitates understanding and solving certain problems of learning from heterogeneous data. In particular, I will talk about basic approaches to causal discovery and address practical issues in causal discovery, including nonstationarity or heterogeneity of the data and existence of measurement error. Finally, I will discuss why and how underlying causal knowledge helps in learning from heterogeneous data when the i.i.d. assumption is dropped, with transfer learning? as a particular example. } }
Endnote
%0 Conference Paper %T Causal Learning and Machine Learning %A Kun Zhang %B Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks %C Proceedings of Machine Learning Research %D 2017 %E Antti Hyttinen %E Joe Suzuki %E Brandon Malone %F pmlr-v73-zhang17a %I PMLR %P 4--4 %U https://proceedings.mlr.press/v73/zhang17a.html %V 73 %X Can we find the causal direction between two variables? How can we make optimal predictions in the presence of distribution shift? We are often faced with such causal modeling or prediction problems. Recently, with the rapid accumulation of huge volumes of data, both causal discovery, i.e., learning causal information from purely observational data, and machine learning are seeing exciting opportunities as well as great challenges. This talk will be focused on recent advances in causal discovery and how causal information facilitates understanding and solving certain problems of learning from heterogeneous data. In particular, I will talk about basic approaches to causal discovery and address practical issues in causal discovery, including nonstationarity or heterogeneity of the data and existence of measurement error. Finally, I will discuss why and how underlying causal knowledge helps in learning from heterogeneous data when the i.i.d. assumption is dropped, with transfer learning? as a particular example.
APA
Zhang, K.. (2017). Causal Learning and Machine Learning. Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, in Proceedings of Machine Learning Research 73:4-4 Available from https://proceedings.mlr.press/v73/zhang17a.html.

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