From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
For practitioners in causal inference and program evaluation, this work provides a method to obtain interpretable and causally valid HTEs even with unmeasured confounders, though the approach is domain-specific to controlled experiments.
The paper addresses heterogeneous treatment effect (HTE) identification in controlled experiments, proposing NEXIS to discover causal, interpretable HTEs from multi-modal pre-treatment data. Applied to two anti-poverty programs in Africa with satellite imagery, it yields novel prescriptive guidelines.
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.