Efficient Intervention Design for Causal Discovery with Latents

Raghavendra Addanki, Shiva Kasiviswanathan, Andrew Mcgregor, Cameron Musco
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:63-73, 2020.

Abstract

We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process. We consider two intervention cost models: (1) a linear cost model where the cost of an intervention on a subset of variables has a linear form, and (2) an identity cost model where the cost of an intervention is the same, regardless of what variables it is on, i.e., the goal is just to minimize the number of interventions. Under the linear cost model, we give an algorithm to identify the ancestral relations of the underlying causal graph, achieving within a $2$-factor of the optimal intervention cost. This approximation factor can be improved to $1+\eps$ for any $\eps > 0$ under some mild restrictions. Under the identity cost model, we bound the number of interventions needed to recover the entire causal graph, including the latent variables, using a parameterization of the causal graph through a special type of colliders. In particular, we introduce the notion of $p$-colliders, that are colliders between pair of nodes arising from a specific type of conditioning in the causal graph, and provide an upper bound on the number of interventions as a function of the maximum number of $p$-colliders between any two nodes in the causal graph.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-addanki20a, title = {Efficient Intervention Design for Causal Discovery with Latents}, author = {Addanki, Raghavendra and Kasiviswanathan, Shiva and Mcgregor, Andrew and Musco, Cameron}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {63--73}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/addanki20a/addanki20a.pdf}, url = {https://proceedings.mlr.press/v119/addanki20a.html}, abstract = {We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process. We consider two intervention cost models: (1) a linear cost model where the cost of an intervention on a subset of variables has a linear form, and (2) an identity cost model where the cost of an intervention is the same, regardless of what variables it is on, i.e., the goal is just to minimize the number of interventions. Under the linear cost model, we give an algorithm to identify the ancestral relations of the underlying causal graph, achieving within a $2$-factor of the optimal intervention cost. This approximation factor can be improved to $1+\eps$ for any $\eps > 0$ under some mild restrictions. Under the identity cost model, we bound the number of interventions needed to recover the entire causal graph, including the latent variables, using a parameterization of the causal graph through a special type of colliders. In particular, we introduce the notion of $p$-colliders, that are colliders between pair of nodes arising from a specific type of conditioning in the causal graph, and provide an upper bound on the number of interventions as a function of the maximum number of $p$-colliders between any two nodes in the causal graph.} }
Endnote
%0 Conference Paper %T Efficient Intervention Design for Causal Discovery with Latents %A Raghavendra Addanki %A Shiva Kasiviswanathan %A Andrew Mcgregor %A Cameron Musco %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-addanki20a %I PMLR %P 63--73 %U https://proceedings.mlr.press/v119/addanki20a.html %V 119 %X We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process. We consider two intervention cost models: (1) a linear cost model where the cost of an intervention on a subset of variables has a linear form, and (2) an identity cost model where the cost of an intervention is the same, regardless of what variables it is on, i.e., the goal is just to minimize the number of interventions. Under the linear cost model, we give an algorithm to identify the ancestral relations of the underlying causal graph, achieving within a $2$-factor of the optimal intervention cost. This approximation factor can be improved to $1+\eps$ for any $\eps > 0$ under some mild restrictions. Under the identity cost model, we bound the number of interventions needed to recover the entire causal graph, including the latent variables, using a parameterization of the causal graph through a special type of colliders. In particular, we introduce the notion of $p$-colliders, that are colliders between pair of nodes arising from a specific type of conditioning in the causal graph, and provide an upper bound on the number of interventions as a function of the maximum number of $p$-colliders between any two nodes in the causal graph.
APA
Addanki, R., Kasiviswanathan, S., Mcgregor, A. & Musco, C.. (2020). Efficient Intervention Design for Causal Discovery with Latents. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:63-73 Available from https://proceedings.mlr.press/v119/addanki20a.html.

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