Optimizing survival outcomes, such as patient survival or customer retention, is a critical objective in data-driven decision-making. Off-Policy Evaluation~(OPE) provides a powerful framework for assessing such decision-making policies using logged data alone, without the need for costly or risky online experiments in high-stakes applications. However, typical estimators are not designed to handle right-censored survival outcomes, as they ignore unobserved survival times beyond the censoring time, leading to systematic underestimation of the true policy performance. To address this issue, we propose a novel framework for OPE and Off-Policy Learning~(OPL) tailored for survival outcomes under censoring. Specifically, we introduce IPCW-IPS and IPCW-DR, which employ the Inverse Probability of Censoring Weighting technique to explicitly deal with censoring bias. We theoretically establish that our estimators are unbiased and that IPCW-DR achieves double robustness, ensuring consistency if either the propensity score or the outcome model is correct. Furthermore, we extend this framework to constrained OPL to optimize policy value under budget constraints. We demonstrate the effectiveness of our proposed methods through simulation studies and illustrate their practical impacts using public real-world data for both evaluation and learning tasks.

Panel data methods are widely used in empirical analysis to address unobserved heterogeneity, but causal inference remains challenging when treatments are endogenous and confounding variables high-dimensional and potentially nonlinear. Standard instrumental variables (IV) estimators, such as two-stage least squares (2SLS), become unreliable when instrument validity requires flexibly conditioning on many covariates with potentially non-linear effects. This paper develops a Double Machine Learning estimator for static panel models with endogenous treatments (panel IV DML), and introduces weak-identification diagnostics for it. We revisit three influential migration studies that use shift-share instruments. In these settings, instrument validity depends on a rich covariate adjustment. In one application, panel IV DML strengthens the predictive power of the instrument and broadly confirms 2SLS results. In the other cases, flexible adjustment makes the instruments weak, leading to substantially more cautious causal inference than conventional 2SLS. Monte Carlo evidence supports these findings, showing that panel IV DML improves estimation accuracy under strong instruments and delivers more reliable inference under weak identification.

Modern surgery has gone from long incisions to tiny cuts guided by robots and AI. In the process, however, surgeons have lost something vital: the chance to feel inside the body directly. Without palpation, it becomes harder to detect tissue abnormalities during an operation. A group of surgeons and engineers across Europe is now trying to bring back this vital aspect of surgery. Working within an EU-funded research collaboration called PALPABLE, they are developing a soft robotic "fingertip" that can sense how firm or soft tissue is during minimally invasive and robotic surgery.

Twelve men have been charged with the manslaughter of football fan Simon Dobbin. Dobbin was assaulted outside the Railway Tavern in Southend-on-Sea in Essex in March 2015 following a match between Cambridge United and Southend United. The dad from Mildenhall, Suffolk, died in October 2020 after suffering a brain injury as a result of the attack. The Crown Prosecution Service (CPS) said it had now decided to prosecute 12 men in connection with his death and all the defendants will appear at Colchester Magistrates' Court on 31 March. Rebecca Mundy, deputy chief crown prosecutor, said the CPS had worked closely with Essex Police to examine and review material obtained during previous investigations.

Baseline Model As a baseline, we use the aforementioned linear Cox proportional hazard model[Cox72].

Science Archaeology Metal detectorist finds medieval pendant with a Roman'secret' The discovery is an artifact within an artifact. Breakthroughs, discoveries, and DIY tips sent six days a week. A discovery on a farm in Essex, England, is a bit of an archaeological version of the 2010 film . In September 2024, a metal detectorist scouring a farm about 45 miles northeast of London found a silver, oval pendant measuring about one-inch-long. The piece included an inscribed frame of mirrored Latin text that allowed for wax impressions.

A fly-tipper dressed as a giant purple T. rex has been caught on camera dumping rubbish in a street. The brightly coloured rogue raptor was spotted checking for traffic before crossing a road in Southend, Essex. The prehistoric predator then looks around before slinging two black bin bags to the ground next to large black bin. Footage of the incident, first reported by Your Southend, was captured on a resident's CCTV just before 21:30 GMT on Tuesday. The city council told the BBC it had not received any reports of fly-tipping in relation to the incident.

Classical machine learning classifiers tend to be overconfident can be unreliable outside of the laboratory benchmarks. Properly assessing the reliability of the output of the model per sample is instrumental for real-life scenarios where these systems are deployed. Because of this, different techniques have been employed to properly quantify the quality of prediction for a given model. These are most commonly Bayesian statistics and, more recently, conformal learning. Given a calibration set, conformal learning can produce outputs that are guaranteed to cover the target class with a desired significance level, and are more reliable than the standard confidence intervals used by Bayesian methods. In this work, we propose to use conformal learning with fuzzy rule-based systems in classification and show some metrics of their performance. Then, we discuss how the use of type 2 fuzzy sets can improve the quality of the output of the system compared to both fuzzy and crisp rules. Finally, we also discuss how the fine-tuning of the system can be adapted to improve the quality of the conformal prediction.