Causal Bias Quantification for Continuous Treatments

2021/06/17 by Gianluca Detommaso, Detommaso, Gianluca, Michael Brückner +5
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistical Methods in Clinical Trials #cs.AI #cs.LG #stat.ME #stat.ML

paper · pdf · doi:10.48550/arxiv.2106.09762

openalex publication_date 2021/06/17 · arxiv created 2022/01/30 · arxiv updated 2022/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We extend the definition of the marginal causal effect to the continuous treatment setting and develop a novel characterization of causal bias in the framework of structural causal models. We prove that our derived bias expression is zero if, and only if, the causal effect is identifiable via covariate adjustment. We show that under some restrictions on the structural equations, the causal bias can be estimated efficiently and allows for causal regularization of predictive probabilistic models. We demonstrate the effectiveness of our method for causal bias quantification in various settings where (not) controlling for certain covariates would introduce causal bias.

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