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This research advances causal machine learning by introducing NEXIS, a method that leverages multi-modal pre-treatment data like satellite imagery to identify unmeasured drivers of policy effectiveness. By reframing heterogeneous treatment effect identification as Markov-blanket discovery, the approach offers provably consistent selection for optimizing real-world interventions such as anti-poverty programs in Africa. The work bridges the gap between high-expressivity models and interpretability, providing a scalable framework for deriving actionable insights from complex experimental data without relying on extensive human supervision.
From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
arXiv:2606.17010v1 Announce Type: new Abstract: Heterogeneous Treatment Effect (HTE) identification is crucial to explain the impact of an intervention and optimize our policies accordingly. Existing approaches trade expressivity for interpretability, but, if some active heterogeneity drivers are unmeasured, methods at both ends of this spectrum allow for spurious HTE characterization with no causal reading. In this work, we focus on controlled experiments and argue that an oracle HTE causal characterization via the latent interactors is now within reach, thanks to (i) more extensive pre-treatment measurements, i.e., multi-modal and multi-view, and (ii) scalable representations with minimal human supervision. We then re-frame HTE identification as a Markov-blanket discovery problem on a sufficient and aligned pre-treatment representation, and introduce Neural EXposure Interaction Search (NEXIS), an iterative procedure with provable and empirically validated consistent selection. We deploy NEXIS on two anti-poverty programs in Africa, augmenting each with satellite imagery capturing previously unmeasured environmental effect modifiers, leading to novel, interpretable and prescriptive guidelines to optimize the programs' next iterations.
Abstract:Heterogeneous Treatment Effect (HTE) identification is crucial to explain the impact of an intervention and optimize our policies accordingly. Existing approaches trade expressivity for interpretability, but, if some active heterogeneity drivers are unmeasured, methods at both ends of this spectrum allow for spurious HTE characterization with no causal reading. In this work, we focus on controlled experiments and argue that an oracle HTE causal characterization via the latent interactors is now within reach, thanks to (i) more extensive pre-treatment measurements, i.e., multi-modal and multi-view, and (ii) scalable representations with minimal human supervision. We then re-frame HTE identification as a Markov-blanket discovery problem on a sufficient and aligned pre-treatment representation, and introduce Neural EXposure Interaction Search (NEXIS), an iterative procedure with provable and empirically validated consistent selection. We deploy NEXIS on two anti-poverty programs in Africa, augmenting each with satellite imagery capturing previously unmeasured environmental effect modifiers, leading to novel, interpretable and prescriptive guidelines to optimize the programs' next iterations.
Machine Learning (cs.LG)
arXiv:2606.17010 [cs.LG]
(or arXiv:2606.17010v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.17010 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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