arXiv research

Modern treatment of space curve evolutes and involutes.

Paper uses ML to improve A/B testing for complex treatment effects.

Modern differential cohomology explained with applications.

A new method generates counterfactual treatment outcomes for time-varying treatments.

We use equivariant surgery to classify all involutions on closed surfaces, up to isomorphism. Work on this problem is classical, dating back to the nineteenth century, but some questions seem to have been left unanswered. We give a modern treatment that leads to a complete classification.

This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expec…

Develops methods to learn optimal treatment regimes using causal tree methods.

Researchers propose better probabilistic models for deep learning.

Paper derives policy rules from observational data for hepatitis C treatment.

New method estimates heterogeneous treatment effects with improved guarantees.

Causal effect estimation relies on separating the variation in the outcome into parts due to the treatment and due to the confounders. To achieve this separation, practitioners often use external sources of randomness that only influence the treatment called instrumental variables (IVs). We study variables constructed …

DiD-BCF model improves causal inference in panel data with robust non-parametric methods.

This article attempts to place the emergence of probabilistic numerics as a mathematical-statistical research field within its historical context and to explore how its gradual development can be related both to applications and to a modern formal treatment. We highlight in particular the parallel contributions of Sul'…

Estimating the long-term effects of treatments is of interest in many fields. A common challenge in estimating such treatment effects is that long-term outcomes are unobserved in the time frame needed to make policy decisions. One approach to overcome this missing data problem is to analyze treatments effects on an int…

Generative framework improves causal estimation from observational data.

This tutorial introduces causal modeling methods for researchers.

Neyman's framework evaluates personalized treatment rules using experiments.

Bandit algorithms optimize treatment decisions for precision medicine.

In this editorial guide for the special issue on econophysics, we give a unique review of this young but quickly growing discipline. A suggestive taxonomy of the development is proposed by making a distinction between classical econophysics and modern econophysics. For each of these two stages of development, we identi…

CausalBGM uses AI to infer causal effects from complex data.

The paper proposes an interpretable off-policy learning algorithm for medical treatments.

FOCaL meta-learner estimates functional treatment effects robustly.

Modern treatment of Winger's pencil reveals deep connections to modular curves and monodromy.

New method estimates hazard ratios without bias in observational studies.

A textbook on statistical machine learning for astronomy.

This paper applies knot theory to modern yo-yo play.

Paper evaluates dynamic QTE for ridesharing data.

Modern CATE models often fail to outperform a trivial zero-effect predictor, highlighting significant challenges.

MOCA uses modular attention to estimate causal effects from complex data.

Paper tackles causal inference with partially labeled data, introducing robust methods.

This paper provides estimation and inference methods for a conditional average treatment effects (CATE) characterized by a high-dimensional parameter in both homogeneous cross-sectional and unit-heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting the base treatment variable w…

Proposes Causal k-Means Clustering to identify subgroup effects.

In this mostly pedagogical tutorial article a brief introduction to modern geometrical treatment of fluid dynamics and electrodynamics is provided. The main technical tool is standard theory of differential forms. In fluid dynamics, the approach is based on general theory of integral invariants (due to Poincare and Car…

New estimator for causal effects in large datasets.

New method uses predictions to infer causal effects without labeled data.

Residual neural networks improve collision prediction in planetary simulations.

This paper bridges statistical and machine learning approaches to variational inference.

Unified RL survey for healthcare AI interventions.

Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by {dropout} and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set tre…

Transformer RL optimizes A/B testing for time series experiments.

Synthetic control method improves policy evaluation in high-dimensional settings.

Proposes a new loss function for causal machine-learning.

MediEncoder learns nonlinear representations for causal mediation analysis.

The majority of cancer treatments end in failure due to Intra-Tumor Heterogeneity (ITH). ITH in cancer is represented by clonal evolution where different sub-clones compete with each other for resources under conditions of Darwinian natural selection. Predicting the growth of these sub-clones within a tumour is among t…

We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localized destruc…

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (…

BGM-IV uses AI to estimate causal effects in complex data.

GraphITE estimates individual effects of graph-structured treatments.