aiqbench analysis
The paper reframes causal effect estimation as a supervised learning problem, so it can handle treatments that can't be enumerated, such as free-form text. Its main technical claim is that estimation error decomposes into moment-balancing errors, which motivates training objectives that target those errors directly. Its evidence is empirical and limited to held-out generalization and comparisons on continuous, discrete, and text treatments, so readers should check how the assumptions of realizability and no unobserved confounding hold in their own data.
Supervised Learning Objectives for High-Dimensional Treatment Effects
arXiv:2605.27281v2 Announce Type: replace Abstract: Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movement in share price, is useful across several domains. However, classical causal estimators tend to assume that all possible interventions are observed, which is infeasible when interventions vary widely, for instance, in the space of all text strings. We adapt a well-known approach of recasting causal inference as a learning problem to address such settings, assuming realizability of the learned function and no unobserved confounding. We show that causal error decomposes into moment-balancing errors of increasing order and exploit structure of the conditional outcome in the confounders to design objectives that directly improve causal estimation. We also show how to project the effect of a high-dimensional treatment onto coarser attributes, which allows a single model to answer several causal questions without additional attribute-specific training. We empirically evaluate our estimators in settings with high-dimensional continuous, discrete, and text treatments. Our experiments demonstrate generalization to held-out treatments, benefits of balancing higher-order moments when required, and projected causal estimates that are competitive with attribute-specific ones.
Abstract:Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movement in share price, is useful across several domains. However, classical causal estimators tend to assume that all possible interventions are observed, which is infeasible when interventions vary widely, for instance, in the space of all text strings. We adapt a well-known approach of recasting causal inference as a learning problem to address such settings, assuming realizability of the learned function and no unobserved confounding. We show that causal error decomposes into moment-balancing errors of increasing order and exploit structure of the conditional outcome in the confounders to design objectives that directly improve causal estimation. We also show how to project the effect of a high-dimensional treatment onto coarser attributes, which allows a single model to answer several causal questions without additional attribute-specific training. We empirically evaluate our estimators in settings with high-dimensional continuous, discrete, and text treatments. Our experiments demonstrate generalization to held-out treatments, benefits of balancing higher-order moments when required, and projected causal estimates that are competitive with attribute-specific ones.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2605.27281 [cs.LG]
(or arXiv:2605.27281v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.27281 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 281 words
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