Deconfounder Algorithm

The goal of Causal Inference is to describe the distribution of potential outcomes to each state of a causal factor. Multiple Causal Inference does this for multiple causal factors / vector of factors.

Multiple Causal Inference

Argument for Deconfounder

SUTVA short for Stable Unit Treatment Value Assumption requires that potential outcomes for one individual are independent of every other individual.

Defintion : SUTVA

Identifying Average Causal Effects of all Causes

Identifying Average Causal Effects of Subsets of Causes

Ignorability

Identification of conditional mean potential outcome

Lemma 3

Lemma 4

University of Michigan - Ann Arbor

1. The blessings of multiple causes - Wang, Blei 2019 : 
2. The challenges of multiple causes -Imai, Jiang 2019 : 


Important Reference Papers

The Blessings of Multiple Causes - Wang, Blei 2019

https://imai.fas.harvard.edu/research/files/deconfounder.pdf

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