Why prediction-driven variable selection misses confounders and how Bayesian Adjustment for Confounding attempts to fix it. The post Why Your Best Predictive Model Gives the Wrong Treatment Effect appeared first on Towards Data Science .
what it costs, what it gains and the three mistakes that I make The post My SciPy ODE Solver Was Killing My Bayesian Inference: A Cosmologist’s Honest Account of Discovering Diffrax appeared first on Towards Data Science .
How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on The post My Fall-Detection Model Scored 94%, and It Was Lying to Me appeared first on Towards Data Science .
arXiv:2607. 04133v1 Announce Type: new Abstract: Causal discovery with nonlinear mechanisms and latent confounders remains challenging.
arXiv:2608. 08288v1 Announce Type: new Abstract: Estimating counterfactual outcomes over time from longitudinal observational data is central to clinical decision support.
arXiv:2607. 09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.
arXiv:2607. 21806v1 Announce Type: new Abstract: Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and criminal justice.
arXiv:2606. 00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models.
arXiv:2605. 13430v3 Announce Type: replace-cross Abstract: Selection bias is pervasive in observational studies.
Posted by Rishabh Tiwari, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research Machine learning models in the real world are often trained on limited data that may contain unintended statistical biases . For example, in the CELEBA celebrity image dataset, a disproportionate number of female celebrities have blond hair, leading to classifiers incorrectly predicting “blond” as the hair color for most female faces — here, gender is a spurious feature for predicting hair color.
arXiv:2607. 03161v1 Announce Type: cross Abstract: In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees need not control reliability on that selected subset.
Small prompt changes can silently break critical behavior in production. This article introduces a practical framework to detect hidden regressions before users notice.
arXiv:2603. 02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding.