论文 | Causal Inference with Unstructured Outcomes：面向文本与图像结果的因果推断

TL;DR - An arXiv preprint (Wibisono & Wang, University of Michigan) extends causal inference to unstructured outcomes like text and images by making "which outcome feature the treatment most changes" the causal question itself, rather than fixing a metric in advance. It matters as more interventions (AI writing tools, prompts, imaging algorithms) produce non-scalar outputs that average treatment effects can't describe.

• Defines the maximally contrastive feature: a bounded scoring function over a candidate function class (e.g. neural nets on embeddings) that maximizes the causal contrast between treated and control potential outcomes; boundedness (typically [0,1]) prevents degenerate scaling.
• Identification relies on standard assumptions — consistency, ignorability, overlap — with propensity-score weighting; estimation uses parametric scoring functions with sample splitting/cross-fitting, plus stated asymptotic normality and efficiency conditions.
• Extensions: covariate-dependent features for heterogeneous effects (treatment direction can reverse across contexts), and paired treatment-side/outcome-side scoring functions when both treatment and outcome are unstructured, trained against matched negative-control outcomes.
• Experiments span text formality, toxicity, multi-attribute text change, cell-image blur (with nudging along the learned direction), synthetic paired-coordinate recovery, and prompt→news-headline generation; authors caution results depend on the representation space and that learned features require post-hoc interpretation, not automatic semantic labels.