This work introduces Masked Neural Causal Models (NCMs), a provably expressive nonparametric framework for simulating and bounding interventional queries over all models compatible with a given CPDAG given observational data, and gives an optimization objective in the space of masked NCMs that asymptotically recovers the true bounds of the causal effect.
Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions. Existing identifiability research primarily focuses on continuous variables under additive noise models, often neglecting mixed datasets...
Sambit Mishra, Ying-Ying Wang, Christine K. Johnson et al.· 1 citation
This work develops a model-free and constraint-query optimal statistical inference framework for causal discovery under latent variables and selection using single-target interventions, and introduces the system-induced subgraph (SIS) to capture the causal relations among system variables while accounting for context v...
Xiao-Tian Hou, Kwangmoon Park, Hong-Zhe Li· 0 citations
Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on conditional independence tests, structure scores, or restrictive functional assumptions, we propose Decoupled Causal Discovery (DCD), a novel decoupling-based perspecti...
Causal foundation models are pretrained neural networks that estimate causal quantities, such as the average treatment effect, on entirely new datasets using in-context learning without requiring model updates.
Christopher Stith, Hossein Rahmani, Jesse C. Cresswell· 0 citations
This paper investigates the development of causal foundation models for bounding the effect of interventions and counterfactuals from observational data. We show that a canonical prior can be defined with full support over the space of structural causal models with discrete observables. With this canonical prior, we tr...
This work considers the task of conditional causal discovery as a Bayesian inference problem, in which the posterior is targeted over causal graphs and parameters conditional on an event such as a causal-effect constraint, and adapts rare-event estimation techniques to perform inference the joint graph-parameter space.
Cixuan Zhang, Guy Van den Broeck, Benjie Wang· 0 citations
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