Multiple Causal Inference with Latent Confounding

By Rajesh Ranganath, Adler J. Perotte. 2018

52 citations recorded by the source corpus.

Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, causal inference assumptions have focused on estimation of effects for a single treatment. In this work, we construct techniques for estimation with multiple treatments in the presence of unobserved confounding. We develop two assumptions based on shared confounding between treatments and independence of treatments given the confounder. Together, these assumptions lead to a confounder estimator regularized by mutual information. For this estimator, we develop a tractable lower bound. To recover treatment effects, we use the residual information in the treatments independent of the confounder. We validate on simulations and an example from clinical medicine.

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1. Dehan Kong
2. Shu Yang
3. Linbo Wang
4. Yixin Wang
5. Ioana Bica
6. Tarid Wongvorachan

Citations in the corpus

• Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “Several other methods have been proposed for taking advantage of the multiplicity of assigned causes in the static setting and capture shared latent confounding (Tran & Blei, 2017; Heckerman, 2018; Ranganath & Perotte, 2018).”
• Multi-cause causal inference with unmeasured confounding and binary outcome. (2019, background): “In a 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, 201”
• Using Text Embeddings for Causal Inference (2019, background): “Work on causal inference with hidden confounding and many treatments is in the same vein [ WB18; RP18; D’A19 ] .”
• Multiple Causes: A Causal Graphical View (2019, background): “Finally, this paper connects to the growing literature on multiple causal inference (Tran & Blei, 2017; Wang & Blei, 2018; Ranganath & Perotte, 2018; Heckerman, 2018; Janzing & Schölkopf, 2018).”
• The Blessings of Multiple Causes (2018, background): “Multiple causal inference and latent confounding was also formalized by Ranganath and Perotte (2018), who take an information-theoretic approach.”