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Found 12 papers, 5 papers with code
Differentiable and Transportable Structure Learning
We are interested in unsupervised structure learning with a particular focus on directed acyclic graphical (DAG) models.
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
While observational data is confounded, randomized data is unconfounded, but its sample size is usually too small to learn heterogeneous treatment effects.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects.
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time
Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time.
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
In this paper, we introduce DECAF: a GAN-based fair synthetic data generator for tabular data.
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects
Conditional average treatment effects (CATEs) allow us to understand the effect heterogeneity across a large population of individuals.
OrganITE: Optimal transplant donor organ offering using an individual treatment effect
Transplant-organs are a scarce medical resource.
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.
Autoencoders for strategic decision support
However, few data-driven tools that support strategic decision-making are available.
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.
