AI RESEARCH
Confounder Detection via Treatment Intent: A New Observational Study Design
arXiv CS.AI
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ArXi:2605.26413v1 Announce Type: cross Understanding the effects of interventions is central to scientific progress, with randomized controlled trials (RCTs) regarded as the gold standard for causal inference in many applied fields. However, RCTs are costly, time-consuming, and often constrained by ethical or practical limitations, motivating the need for causal methods able to draw