Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score updates, cutting run-times over prior algorithms. This makes it feasible to fully embrace discrete search, enabling iterated local search with principled order initialization to find graphs with scores at or close to the global optimum. The resulting structures are highly accurate across benchmarks, with near-perfect recovery in standard settings. This performance calls for revisiting discrete search over graphs as a reasonable approach to causal discovery.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack
We argue that immature data pipelines are preventing a large portion of industry practitioners from leveraging the latest research on recommender systems. We propose our template data stack for machine learning at "reaso…
BIG-bench Machine LearningRecommendation SystemsBeyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets
This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCBNB-MB), a novel causal discovery algorithm for time series that relaxes the common assumption of a single, tim…
Time Series AnalysisEmbracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach
This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual ca…
CATE: CAusality Tree Extractor from Natural Language Requirements
Causal relations (If A, then B) are prevalent in requirements artifacts. Automatically extracting causal relations from requirements holds great potential for various RE activities (e.g., automatic derivation of suitable…
RelationSentenceTree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery
Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics. In this work, we propose CD-UCT, a model-based reinforcement learning method for causal discovery ba…
Causal DiscoveryDecision MakingModel-based Reinforcement Learningreinforcement-learning+1