TCPFN is a zero-shot foundation model for temporal causal discovery on panel data that jointly predicts multiple causal aspects and reliability signals, with reported high AUROC on benchmarks and better scaling than PCMCI on large panels.
Foundation models for causal inference via prior-data fitted networks.arXiv preprint arXiv:2506.10914
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 4
citation-polarity summary
verdicts
UNVERDICTED 8roles
background 3polarities
background 3representative citing papers
The paper defines computational identifiability as success of a finite search procedure in finding an empirical estimator for a causal query within error tolerance, conditional on the search assumptions and procedure.
A prior-data fitted network amortizes causal sensitivity analysis by generating training labels via Lagrangian scalarization, achieving orders-of-magnitude faster bounds computation than per-instance methods.
TabDistill distills feature interactions from tabular foundation models via post-hoc attribution and inserts them into GAMs, yielding consistent predictive gains.
TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than TabPFN-2.5.
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
CausalTimePrior generates synthetic temporal structural causal models with paired observational and interventional time series to train prior-data fitted networks for in-context causal effect estimation on held-out data.
TabPFN-2.5 scales tabular foundation models to 20x larger datasets, outperforms tuned tree models on TabArena, achieves near-perfect win rates against default XGBoost, and adds a distillation engine for fast production deployment.
citing papers explorer
-
Temporal Causal Prior-Data Fitted Networks for Panel Data with Learned Reliability Signals
TCPFN is a zero-shot foundation model for temporal causal discovery on panel data that jointly predicts multiple causal aspects and reliability signals, with reported high AUROC on benchmarks and better scaling than PCMCI on large panels.
-
Computational Identifiability
The paper defines computational identifiability as success of a finite search procedure in finding an empirical estimator for a causal query within error tolerance, conditional on the search assumptions and procedure.
-
Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models
TabDistill distills feature interactions from tabular foundation models via post-hoc attribution and inserts them into GAMs, yielding consistent predictive gains.
-
TabPFN-3: Technical Report
TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than TabPFN-2.5.
-
Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
-
Interventional Time Series Priors for Causal Foundation Models
CausalTimePrior generates synthetic temporal structural causal models with paired observational and interventional time series to train prior-data fitted networks for in-context causal effect estimation on held-out data.
-
TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
TabPFN-2.5 scales tabular foundation models to 20x larger datasets, outperforms tuned tree models on TabArena, achieves near-perfect win rates against default XGBoost, and adds a distillation engine for fast production deployment.