Causal Inference for Latent Outcomes Learned with Factor Models

2025/06/25 by Jenna M. Landy, Dafne Zorzetto, Landy, Jenna M. +5 · 2 citations
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.2506.20549

openalex publication_date 2025/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In many fields\unicodex2013including genomics, epidemiology, natural language processing, social and behavioral sciences, and economics\unicodex2013it is increasingly important to address causal questions in the context of factor models or representation learning. In this work, we investigate causal effects on latent outcomes derived from high-dimensional observed data using nonnegative matrix factorization. To the best of our knowledge, this is the first study to formally address causal inference in this setting. A central challenge is that estimating a latent factor model can cause an individual's learned latent outcome to depend on other individuals' treatments, thereby violating the standard causal inference assumption of no interference. We formalize this issue as learning-induced interference and distinguish it from interference present in a data-generating process. To address this, we propose a novel, intuitive, and theoretically grounded algorithm to estimate causal effects on latent outcomes while mitigating learning-induced interference and improving estimation efficiency. We establish theoretical guarantees for the consistency of our estimator and demonstrate its practical utility through simulation studies and an application to cancer mutational signature analysis. All baseline and proposed methods are available in our open-source R package, \tt causalLFO.

Citations

• Multivariate Causal Effects: a Bayesian Causal Regression Factor Model
• Spatio-temporal quasi-experimental methods for rare disease outcomes: The impact of reformulated gasoline on childhood hematologic cancer
• Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
• Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
• How to Make Causal Inferences Using Texts
• Nonnegative Matrix Factorization: A Comprehensive Review
• Mutational Processes Molding the Genomes of 21 Breast Cancers
• ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*
• Estimating causal effects of treatments in randomized and nonrandomized studies.

Cited by

• Nonparametric Identification and Estimation of Causal Effects on Latent Outcomes
• Poisson process factorization for mutational signature analysis with genomic covariates

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