A Strategy for Making Predictions Under Manipulation

Laura E. Brown, Ioannis Tsamardinos
; Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008, PMLR 3:35-52, 2008.

Abstract

The first Causality Challenge competition posted several causal discovery problems that require researchers to employ the full arsenal of state-of-the-art causal discovery methods, while prompting the development of new ones. Our approach used the formalism of Causal Bayesian Networks to model and induce causal relations and to make predictions about the effects of the manipulation of the variables. Using state-of-the-art, under development, or newly invented methods specifically for the purposes of the competition, we addressed the following problems in turn in order to build and evaluate a model: (a) finding the Markov Blanket of the target even under some non-faithfulness conditions (e.g., parity functions), (b) reducing the problems to a size manageable by subsequent algorithms, (c) identifying and orienting the network edges, (d) identifying causal edges (i.e., not confounded), and (e) selecting the causal Markov Blanket of the target in the manipulated distribution. The results of the competition illustrate some of the strengths and weaknesses of the state-of-the-art of causal discovery methods and point to new directions in the field. An implementation of our approach is available at http://www.dsl-lab.org for use by other researchers.

Cite this Paper


BibTeX
@InProceedings{pmlr-v3-brown08a, title = {A Strategy for Making Predictions Under Manipulation}, author = {Laura E. Brown and Ioannis Tsamardinos}, booktitle = {Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008}, pages = {35--52}, year = {2008}, editor = {Isabelle Guyon and Constantin Aliferis and Greg Cooper and André Elisseeff and Jean-Philippe Pellet and Peter Spirtes and Alexander Statnikov}, volume = {3}, series = {Proceedings of Machine Learning Research}, address = {Hong Kong}, month = {03--04 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v3/brown08a/brown08a.pdf}, url = {http://proceedings.mlr.press/v3/brown08a.html}, abstract = {The first Causality Challenge competition posted several causal discovery problems that require researchers to employ the full arsenal of state-of-the-art causal discovery methods, while prompting the development of new ones. Our approach used the formalism of Causal Bayesian Networks to model and induce causal relations and to make predictions about the effects of the manipulation of the variables. Using state-of-the-art, under development, or newly invented methods specifically for the purposes of the competition, we addressed the following problems in turn in order to build and evaluate a model: (a) finding the Markov Blanket of the target even under some non-faithfulness conditions (e.g., parity functions), (b) reducing the problems to a size manageable by subsequent algorithms, (c) identifying and orienting the network edges, (d) identifying causal edges (i.e., not confounded), and (e) selecting the causal Markov Blanket of the target in the manipulated distribution. The results of the competition illustrate some of the strengths and weaknesses of the state-of-the-art of causal discovery methods and point to new directions in the field. An implementation of our approach is available at http://www.dsl-lab.org for use by other researchers.} }
Endnote
%0 Conference Paper %T A Strategy for Making Predictions Under Manipulation %A Laura E. Brown %A Ioannis Tsamardinos %B Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 %C Proceedings of Machine Learning Research %D 2008 %E Isabelle Guyon %E Constantin Aliferis %E Greg Cooper %E André Elisseeff %E Jean-Philippe Pellet %E Peter Spirtes %E Alexander Statnikov %F pmlr-v3-brown08a %I PMLR %J Proceedings of Machine Learning Research %P 35--52 %U http://proceedings.mlr.press %V 3 %W PMLR %X The first Causality Challenge competition posted several causal discovery problems that require researchers to employ the full arsenal of state-of-the-art causal discovery methods, while prompting the development of new ones. Our approach used the formalism of Causal Bayesian Networks to model and induce causal relations and to make predictions about the effects of the manipulation of the variables. Using state-of-the-art, under development, or newly invented methods specifically for the purposes of the competition, we addressed the following problems in turn in order to build and evaluate a model: (a) finding the Markov Blanket of the target even under some non-faithfulness conditions (e.g., parity functions), (b) reducing the problems to a size manageable by subsequent algorithms, (c) identifying and orienting the network edges, (d) identifying causal edges (i.e., not confounded), and (e) selecting the causal Markov Blanket of the target in the manipulated distribution. The results of the competition illustrate some of the strengths and weaknesses of the state-of-the-art of causal discovery methods and point to new directions in the field. An implementation of our approach is available at http://www.dsl-lab.org for use by other researchers.
RIS
TY - CPAPER TI - A Strategy for Making Predictions Under Manipulation AU - Laura E. Brown AU - Ioannis Tsamardinos BT - Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 PY - 2008/12/31 DA - 2008/12/31 ED - Isabelle Guyon ED - Constantin Aliferis ED - Greg Cooper ED - André Elisseeff ED - Jean-Philippe Pellet ED - Peter Spirtes ED - Alexander Statnikov ID - pmlr-v3-brown08a PB - PMLR SP - 35 DP - PMLR EP - 52 L1 - http://proceedings.mlr.press/v3/brown08a/brown08a.pdf UR - http://proceedings.mlr.press/v3/brown08a.html AB - The first Causality Challenge competition posted several causal discovery problems that require researchers to employ the full arsenal of state-of-the-art causal discovery methods, while prompting the development of new ones. Our approach used the formalism of Causal Bayesian Networks to model and induce causal relations and to make predictions about the effects of the manipulation of the variables. Using state-of-the-art, under development, or newly invented methods specifically for the purposes of the competition, we addressed the following problems in turn in order to build and evaluate a model: (a) finding the Markov Blanket of the target even under some non-faithfulness conditions (e.g., parity functions), (b) reducing the problems to a size manageable by subsequent algorithms, (c) identifying and orienting the network edges, (d) identifying causal edges (i.e., not confounded), and (e) selecting the causal Markov Blanket of the target in the manipulated distribution. The results of the competition illustrate some of the strengths and weaknesses of the state-of-the-art of causal discovery methods and point to new directions in the field. An implementation of our approach is available at http://www.dsl-lab.org for use by other researchers. ER -
APA
Brown, L.E. & Tsamardinos, I.. (2008). A Strategy for Making Predictions Under Manipulation. Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008, in PMLR 3:35-52

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