%0 Journal Article %1 wang2019multiple %A Wang, Yixin %A Blei, David M. %D 2019 %K causal-analysis %T Multiple Causes: A Causal Graphical View %U http://arxiv.org/abs/1905.12793 %X Unobserved confounding is a major hurdle for causal inference from observational data. Confounders---the variables that affect both the causes and the outcome---induce spurious non-causal correlations between the two. Wang & Blei (2018) lower this hurdle with "the blessings of multiple causes," where the correlation structure of multiple causes provides indirect evidence for unobserved confounding. They leverage these blessings with an algorithm, called the deconfounder, that uses probabilistic factor models to correct for the confounders. In this paper, we take a causal graphical view of the deconfounder. In a graph that encodes shared confounding, we show how the multiplicity of causes can help identify intervention distributions. We then justify the deconfounder, showing that it makes valid inferences of the intervention. Finally, we expand the class of graphs, and its theory, to those that include other confounders and selection variables. Our results expand the theory in Wang & Blei (2018), justify the deconfounder for causal graphs, and extend the settings where it can be used.

@article{wang2019multiple, abstract = {Unobserved confounding is a major hurdle for causal inference from observational data. Confounders---the variables that affect both the causes and the outcome---induce spurious non-causal correlations between the two. Wang & Blei (2018) lower this hurdle with "the blessings of multiple causes," where the correlation structure of multiple causes provides indirect evidence for unobserved confounding. They leverage these blessings with an algorithm, called the deconfounder, that uses probabilistic factor models to correct for the confounders. In this paper, we take a causal graphical view of the deconfounder. In a graph that encodes shared confounding, we show how the multiplicity of causes can help identify intervention distributions. We then justify the deconfounder, showing that it makes valid inferences of the intervention. Finally, we expand the class of graphs, and its theory, to those that include other confounders and selection variables. Our results expand the theory in Wang & Blei (2018), justify the deconfounder for causal graphs, and extend the settings where it can be used.}, added-at = {2019-06-04T18:00:18.000+0200}, author = {Wang, Yixin and Blei, David M.}, biburl = {https://www.bibsonomy.org/bibtex/27152f59cfa714e2beea84f343c62368b/kirk86}, description = {[1905.12793] Multiple Causes: A Causal Graphical View}, interhash = {eb3ad96e6a9d4434d4878b597e22631f}, intrahash = {7152f59cfa714e2beea84f343c62368b}, keywords = {causal-analysis}, note = {cite arxiv:1905.12793Comment: 23 pages}, timestamp = {2019-06-04T18:00:18.000+0200}, title = {Multiple Causes: A Causal Graphical View}, url = {http://arxiv.org/abs/1905.12793}, year = 2019 }