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Found 27 papers, 14 papers with code
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
Research on continual graph learning for AML, however, is still scarce.
Uplift modeling with continuous treatments: A predict-then-optimize approach
One common approach involves two steps: first, an inference step that estimates conditional average treatment effects (CATEs), and second, an optimization step that ranks entities based on their CATE values and assigns treatment to the top k within a given budget.
Optimizing Treatment Allocation in the Presence of Interference
In Influence Maximization (IM), the objective is to -- given a budget -- select the optimal set of entities in a network to target with a treatment so as to maximize the total effect.
Using dynamic loss weighting to boost improvements in forecast stability
Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available.
Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
We apply this scheme to eight popular CADR estimators on four widely-used benchmark datasets, running nearly 1, 500 individual experiments.
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation
We conclude that most research relies on expert-based rules and manual features, while deep learning methods have been gaining traction.
Ranked #1 on
Fraud Detection
on Elliptic Dataset
Network analytics for insurance fraud detection: a critical case study
However, network analytics has only recently been applied to fraud detection in the actuarial literature.
Metalearners for Ranking Treatment Effects
We propose an alternative approach based on learning to rank.
Uplift vs. predictive modeling: a theoretical analysis
Despite the growing popularity of machine-learning techniques in decision-making, the added value of causal-oriented strategies with respect to pure machine-learning approaches has rarely been quantified in the literature.
Using representation balancing to learn conditional-average dose responses from clustered data
Estimating a unit's responses to interventions with an associated dose, the "conditional average dose response" (CADR), is relevant in a variety of domains, from healthcare to business, economics, and beyond.
A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions
In lending, where prices are specific to both customers and products, having a well-functioning personalized pricing policy in place is essential to effective business making.
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Machine learning (ML) holds great potential for accurately forecasting treatment outcomes over time, which could ultimately enable the adoption of more individualized treatment strategies in many practical applications.
Timing Process Interventions with Causal Inference and Reinforcement Learning
Our contribution consists of experiments on timed process interventions with synthetic data that renders genuine online RL and the comparison to CI possible, and allows for an accurate evaluation of the results.
Client Recruitment for Federated Learning in ICU Length of Stay Prediction
In this work, we address the step prior to the initiation of a federated network for model training, client recruitment.
Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field
In addition, we build a framework for fraud analytical methods and propose a keywording strategy for future research.
Prescriptive maintenance with causal machine learning
This work proposes to relax both assumptions by learning the effect of maintenance conditional on a machine's characteristics from observational data on similar machines using existing methodologies for causal inference.
A new perspective on classification: optimally allocating limited resources to uncertain tasks
A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain.
To do or not to do: cost-sensitive causal decision-making
In this article, we therefore extend upon the expected value framework and formally introduce a cost-sensitive decision boundary for double binary causal classification, which is a linear function of the estimated individual treatment effect, the positive outcome probability and the cost and benefit parameters of the problem setting.
Weight-of-evidence 2.0 with shrinkage and spline-binning
We present the results of a series of experiments in a fraud detection setting, which illustrate the effectiveness of the presented approach.
HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks
Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks.
The foundations of cost-sensitive causal classification
The framework is shown to instantiate to application-specific cost-sensitive performance measures that have been recently proposed for evaluating customer retention and response uplift models, and allows to maximize profitability when adopting a causal classification model for optimizing decision-making.
Misclassification cost-sensitive ensemble learning: A unifying framework
Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs.
Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
Fraud detection is to be acknowledged as an instance-dependent cost-sensitive classification problem, where the costs due to misclassification vary between instances, and requiring adapted approaches for learning a classification model.
Applications
Autoencoders for strategic decision support
However, few data-driven tools that support strategic decision-making are available.
Learning to rank for uplift modeling
We propose a unified formalisation of different global uplift modeling measures in use today and explore how these can be integrated into the learning-to-rank framework.
Optimising Individual-Treatment-Effect Using Bandits
Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community.
Causal Simulations for Uplift Modeling
Hence, methods are being developed that are able to learn from newly gained experience, as well as handle drifting environments.
