When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation
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arXiv:2609.17238v1 Announce Type: cross Abstract: High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification become...
arXiv:2503. 20546v2 Announce Type: replace-cross Abstract: We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables.
arXiv:2411.05625v2 Announce Type: replace Abstract: We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variable pai...
Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression proposes Loscar, an extension of the RESIT framework to location-scale noise models (LSNMs). Loscar estimates conditional mean and scale using quantile regression, allowing pretrained tabular foundation models to serve as plug‑in estimators. Experiments on bivariate and multivariate causal‑discovery benchmarks show that in‑context quantile regression outperforms a feasible generalized least squares (FGLS) variant, especially when causes are multivariate.
The paper investigates why causal foundation models (CFMs) produce poorly calibrated credible intervals when aggregating unit-level estimates over groups. It shows that CFMs combine unit-level variances assuming independence, but posterior uncertainty induces correlations that shrink the combined variance, leading to coverage loss that worsens with group size. The authors demonstrate that neural process models, which jointly predict for groups, correct this bias and markedly improve interval coverage across synthetic and benchmark datasets.
arXiv:2602. 22083v2 Announce Type: replace-cross Abstract: Causal identification functionals often require integration over conditional densities of continuous variables, such as those arising in nonparametric identification theory of total and mediated causal effects in DAGs with hidden variables.