Amortized Causal Discovery with Prior-Fitted Networks
In recent years, differentiable penalized likelihood methods have gained popularity, optimizing the causal structure by maximizing its likelihood with respect to the data. However, recent research has shown that errors in likelihood estimation, even on relatively large sample sizes, disallow the discovery of proper structures. We propose a new approach to amortized causal discovery that addresses the limitations of likelihood estimator accuracy. Our method leverages Prior-Fitted Networks (PFNs) to amortize data-dependent likelihood estimation, yielding more reliable scores for structure learning. Experiments on synthetic, simulated, and real-world datasets show significant gains in structure recovery compared to standard baselines. Furthermore, we demonstrate directly that PFNs provide more accurate likelihood estimates than conventional neural network-based approaches.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods for causal sensitivity analysis are per-instance procedures, meaning tha…
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
On time-series data, most causal discovery methods fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information which is lost when following …
Causal DiscoveryTime SeriesTime Series AnalysisFoundCause: Causal Discovery with Latent Confounders from Observational Data
Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions. We propose FoundCause, an amortized causal discovery model trained …
Demystifying amortized causal discovery with transformers
Supervised learning approaches for causal discovery from observational data often achieve competitive performance despite seemingly avoiding explicit assumptions that traditional methods make for identifiability. In this…
Causal DiscoveryCausal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction
Longitudinal treatment decisions from multivariate time-series data require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and …
Causal Inference