Multiple Causes: A Causal Graphical View
By Yixin Wang, David M. Blei. 2019
10 citations recorded by the source corpus.
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.
Subfields: Statistics and Probability
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Citations in the corpus
- Multi-cause causal inference with unmeasured confounding and binary outcome. (2019, background): “…recent stream of literature, several authors suggest an alternative approach to this problem by assuming shared confounding between multiple treatments and independence of treatments given the confounder (Wang and Blei, 2019a; Tran and Blei, 2017; Ranganath and Perotte, 2018; Wang and Blei, 2019b).”