Discover Local Causal Network around a Target to a Given Depth

You Zhou, Changzhang Wang, Jianxin Yin, Zhi Geng
Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:191-202, 2010.

Abstract

For a given target node $T$ and a given depth $k \geq 1$, we propose an algorithm for discovering a local causal network around the target $T$ to depth $k$. In our algorithm, we find parents, children and some descendants (PCD) of nodes stepwise away from the target $T$ until all edges within the depth $k$ local network cannot be oriented further. Our algorithm extends the PCD-by-PCD algorithm for prediction with intervention presented in Yin et al. (2008). Our algorithm can construct a local network to depth $k$, has a more efficient stop rule and finds PCDs along some but not all paths starting from the target.

Cite this Paper

BibTeX
@InProceedings{pmlr-v6-zhou10a, title = {Discover Local Causal Network around a Target to a Given Depth}, author = {Zhou, You and Wang, Changzhang and Yin, Jianxin and Geng, Zhi}, booktitle = {Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008}, pages = {191--202}, year = {2010}, editor = {Guyon, Isabelle and Janzing, Dominik and Schölkopf, Bernhard}, volume = {6}, series = {Proceedings of Machine Learning Research}, address = {Whistler, Canada}, month = {12 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v6/zhou10a/zhou10a.pdf}, url = {https://proceedings.mlr.press/v6/zhou10a.html}, abstract = {For a given target node $T$ and a given depth $k \geq 1$, we propose an algorithm for discovering a local causal network around the target $T$ to depth $k$. In our algorithm, we find parents, children and some descendants (PCD) of nodes stepwise away from the target $T$ until all edges within the depth $k$ local network cannot be oriented further. Our algorithm extends the PCD-by-PCD algorithm for prediction with intervention presented in Yin et al. (2008). Our algorithm can construct a local network to depth $k$, has a more efficient stop rule and finds PCDs along some but not all paths starting from the target.} }
Endnote
%0 Conference Paper %T Discover Local Causal Network around a Target to a Given Depth %A You Zhou %A Changzhang Wang %A Jianxin Yin %A Zhi Geng %B Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 %C Proceedings of Machine Learning Research %D 2010 %E Isabelle Guyon %E Dominik Janzing %E Bernhard Schölkopf %F pmlr-v6-zhou10a %I PMLR %P 191--202 %U https://proceedings.mlr.press/v6/zhou10a.html %V 6 %X For a given target node $T$ and a given depth $k \geq 1$, we propose an algorithm for discovering a local causal network around the target $T$ to depth $k$. In our algorithm, we find parents, children and some descendants (PCD) of nodes stepwise away from the target $T$ until all edges within the depth $k$ local network cannot be oriented further. Our algorithm extends the PCD-by-PCD algorithm for prediction with intervention presented in Yin et al. (2008). Our algorithm can construct a local network to depth $k$, has a more efficient stop rule and finds PCDs along some but not all paths starting from the target.
RIS
TY - CPAPER TI - Discover Local Causal Network around a Target to a Given Depth AU - You Zhou AU - Changzhang Wang AU - Jianxin Yin AU - Zhi Geng BT - Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 DA - 2010/02/18 ED - Isabelle Guyon ED - Dominik Janzing ED - Bernhard Schölkopf ID - pmlr-v6-zhou10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 6 SP - 191 EP - 202 L1 - http://proceedings.mlr.press/v6/zhou10a/zhou10a.pdf UR - https://proceedings.mlr.press/v6/zhou10a.html AB - For a given target node $T$ and a given depth $k \geq 1$, we propose an algorithm for discovering a local causal network around the target $T$ to depth $k$. In our algorithm, we find parents, children and some descendants (PCD) of nodes stepwise away from the target $T$ until all edges within the depth $k$ local network cannot be oriented further. Our algorithm extends the PCD-by-PCD algorithm for prediction with intervention presented in Yin et al. (2008). Our algorithm can construct a local network to depth $k$, has a more efficient stop rule and finds PCDs along some but not all paths starting from the target. ER -
APA
Zhou, Y., Wang, C., Yin, J. & Geng, Z.. (2010). Discover Local Causal Network around a Target to a Given Depth. Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, in Proceedings of Machine Learning Research 6:191-202 Available from https://proceedings.mlr.press/v6/zhou10a.html.

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