arXiv research

Random forest model predicts tennis match outcomes with 80% accuracy.

Paper learns data-driven organ matching rules from observational data.

New algorithms learn stable matchings from uncertain user preferences.

We compare various extensions of the Bradley-Terry model and a hierarchical Poisson log-linear model in terms of their performance in predicting the outcome of soccer matches (win, draw, or loss). The parameters of the Bradley-Terry extensions are estimated by maximizing the log-likelihood, or an appropriately penalize…

Bayesian model infers strengths from noisy tennis match outcomes.

Transforming sparse outcomes into dense process rewards for efficient reinforcement learning.

New methods use vector search and nearest-neighbor matching for policy learning in causal inference.

A2A metric evaluates bias correction methods, reducing ATE estimation errors.

Crowd opinions in microblogs can predict event outcomes, matching with expert opinions.

Cricket betting is a multi-billion dollar market. Therefore, there is a strong incentive for models that can predict the outcomes of games and beat the odds provided by bookers. The aim of this study was to investigate to what degree it is possible to predict the outcome of cricket matches. The target competition was t…

PO-Flow models potential and counterfactual outcomes for personalized treatment decisions.

Study uses complex networks and machine learning to predict soccer match outcomes.

DoubleGen addresses bias in generative modeling of counterfactuals.

Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?" questions, such as "What would be the outcome if we gave this patient treatment $t_…

Study shows how competition affects learning in matching markets, proving it's possible to balance stability, fairness, and regret.

New method debiases counterfactual distributions using observational data.

We aim to create the highest possible quality of treatment-control matches for categorical data in the potential outcomes framework. Matching methods are heavily used in the social sciences due to their interpretability, but most matching methods do not pass basic sanity checks: they fail when irrelevant variables are …

Two new estimators reduce costs and improve accuracy for EHR outcome prediction.

New method uses latent variables to estimate treatment effects from single-arm trials.

Study shows refugee matching gains are robust to different evaluation methods.

Matched Machine Learning combines machine learning and matching for causal inference.

Optimizes data labeling for causal effect estimation with missing outcomes.

This paper improves change-point detection for complex data streams using denoising score matching.

FSRM method improves treatment effect estimation from observational data.

We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics. Specifically, we consider tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predic…

Develops a framework for clustering and distribution matching with bandit feedback.

New framework estimates treatment effects based on preferences.

Algorithm solves two-sided matching markets with unknown preferences and constraints.

We provide a simple and efficient algorithm for adversarial $k$-action $d$-outcome non-degenerate locally observable partial monitoring game for which the $n$-round minimax regret is bounded by $6(d+1) k^{3/2} \sqrt{n \log(k)}$, matching the best known information-theoretic upper bound. The same algorithm also achieves…

Turtle Score analyzes developer similarity to match high-performing candidates.

Study proposes new methods to convert betting odds into accurate probabilities for sports forecasting.

In a seminal paper Abadie, Diamond, and Hainmueller [2010] (ADH), see also Abadie and Gardeazabal [2003], Abadie et al. [2014], develop the synthetic control procedure for estimating the effect of a treatment, in the presence of a single treated unit and a number of control units, with pre-treatment outcomes observed f…

Unified theory for causal inference using various methods.

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop algorithms for contextual …

Paper tackles non-monotonic resource utilization in sequential decision-making.

SARD improves deep learning clinical prediction performance.

New algorithms adaptively calibrate predictions in non-stationary environments, matching optimal rates.

Machine learning algorithms are increasingly involved in sensitive decision-making process with adversarial implications on individuals. This paper presents mdfa, an approach that identifies the characteristics of the victims of a classifier's discrimination. We measure discrimination as a violation of multi-differenti…

Bayesian method improves dictionary learning for complex problems.

Optimizes long-term social welfare in recommender systems by matching users to providers.

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles like random forests (Strobl, Malley, and Tutz, 2009) are a useful tool for finding st…

New methods combine machine learning with doubly robust estimators for better treatment effect estimation.

The paper audits trading filters, finding a high save-to-miss ratio.

Quantum neural networks improve causal inference in biomedical studies, especially for small samples.

Study on learning strategies in matching markets with uncertain preferences.

A single slow-growing tree matches Random Forest's performance.

The paper shows how shared random seeds can reduce variance in machine learning evaluations.

We consider the problem faced by a service platform that needs to match limited supply with demand but also to learn the attributes of new users in order to match them better in the future. We introduce a benchmark model with heterogeneous "workers" (demand) and a limited supply of "jobs" that arrive over time. Job typ…