The Blessings of Multiple Causes

Abstract

Causal inference from observational data often assumes "ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that combines unsupervised machine learning and predictive model checking to perform causal inference in multiple-cause settings. The deconfounder infers a latent variable as a substitute for unobserved confounders and then uses that substitute to perform causal inference. We develop theory for the deconfounder, and show that it requires weaker assumptions than classical causal inference. We analyze its performance in three types of studies: semi-simulated data around smoking and lung cancer, semi-simulated data around genome-wide association studies, and a real dataset about actors and movie revenue. The deconfounder provides a checkable approach to estimating closer-to-truth causal effects.

arXiv comment72 pages

References in corpus (2)

• On the definition of a confounder
• Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder

Cited by in corpus (13)

• Causal Confusion in Imitation Learning
• Using Embeddings to Correct for Unobserved Confounding in Networks
• Multiple Causal Inference with Latent Confounding
• Spectral Deconfounding via Perturbed Sparse Linear Models
• Sufficient Representations for Categorical Variables
• Equal Opportunity and Affirmative Action via Counterfactual Predictions
• Multiple Causes: A Causal Graphical View
• The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records
• Learning Individual Causal Effects from Networked Observational Data
• Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding
• Towards A Domain-Customized Automated Machine Learning Framework For Networks and Systems
• Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding
• The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)