Causal Bias Quantification for Continuous Treatments

Causal Bias Quantification for Continuous Treatments

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 covari...

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Hauptverfasser: Detommaso, Gianluca, Brückner, Michael, Schulz, Philip, Chernozhukov, Victor
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Computer Science - Artificial Intelligence
Computer Science - Learning
Statistics - Machine Learning
Statistics - Methodology
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creator Detommaso, Gianluca
Brückner, Michael
Schulz, Philip
Chernozhukov, Victor
description 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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subjects Computer Science - Artificial Intelligence
Computer Science - Learning
Statistics - Machine Learning
Statistics - Methodology
title Causal Bias Quantification for Continuous Treatments
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