It is shown that, in the sample limit, I-FLOP recovers a DAG in the same interventional Markov equivalence class as the data-generating DAG, where it performs favorably in terms of both performance and run time.
Abstract
We extend the FLOP (fast learning of order and parents) algorithm recently proposed by Wien\"obst et al. (2026) from observational to interventional data. In particular, we use the interventional BIC score of Hauser and B\"uhlmann (2012), adapting it to be used with the iterative Cholesky-based score updates that are partly responsible for FLOP's speed. We show that, in the sample limit, I-FLOP recovers a DAG in the same interventional Markov equivalence class as the data-generating DAG. We compare I-FLOP to existing causal structure learning algorithms on real and simulated interventional data, where it performs favorably in terms of both performance and run time.
Predicting how a patient's vital signs respond to an intervention is a central question in intensive care. World models can do so by learning dynamics as a function of prior actions. However, such models tend to be brittle outside of training data. Clinicians choose drug dosages based on the patient's state, so the ass...
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RECaST-Surv is proposed, a Bayesian transfer-learning framework for time-to-event outcomes that extends the RECaST method to survival settings and maintains near-nominal Type I error across challenging settings while improving power by roughly 10%--12% over standard analyses.
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Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
