Search Results for author:
Found 19 papers, 12 papers with code
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
Above all, we consider the introduction of a completely new type of solution to be our most important contribution as it may spark entirely new innovations in treatment effects in general.
DAGnosis: Localized Identification of Data Inconsistencies using Structures
Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models.
GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure
Specifically, we introduce GOGGLE, an end-to-end message passing scheme that jointly learns the relational structure and corresponding functional relationships as the basis of generating synthetic samples.
Learning continuous-valued treatment effects through representation balancing
In this work, we propose CBRNet, a causal machine learning approach to estimate an individual dose response from observational data.
Learning Representations without Compositional Assumptions
This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement.
Causal Deep Learning
Our framework clearly identifies which assumptions are testable and which ones are not, such that the resulting solutions can be judiciously adopted in practice.
Navigating causal deep learning
With CDL, researchers aim to structure and encode causal knowledge in the extremely flexible representation space of deep learning models.
Differentiable and Transportable Structure Learning
Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure.
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
However, no imputation at all also leads to biased estimates, as missingness determined by treatment introduces bias in covariates.
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.
