A Neural Framework for Generalized Causal Sensitivity Analysis

Published: 16 Jan 2024, Last Modified: 15 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Causal machine learning, treatment effect estimation, sensitivity analysis, unobserved confounding, uncertainty estimation
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TL;DR: We propose a neural framework for causal sensitivity analysis under unobserved confounding that is applicable to a variety of sensitivity models, treatment types, and causal queries.
Abstract: Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to draw causal conclusions under unobserved confounding with mathematical guarantees. In this paper, we propose NeuralCSA, a neural framework for generalized causal sensitivity analysis. Unlike previous work, our framework is compatible with (i) a large class of sensitivity models, including the marginal sensitivity model, $f$-sensitivity models, and Rosenbaum's sensitivity model; (ii) different treatment types (i.e., binary and continuous); and (iii) different causal queries, including (conditional) average treatment effects and simultaneous effects on multiple outcomes. This generality is achieved by learning a latent distribution shift that corresponds to a treatment intervention using two conditional normalizing flows. We provide theoretical guarantees that NeuralCSA is able to infer valid bounds on the causal query of interest and also demonstrate this empirically using both simulated and real-world data.
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Primary Area: causal reasoning
Submission Number: 3435