Search Results for author: Alicia Curth

Found 15 papers, 11 papers with code

Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time

1 code implementation7 Jun 2023 Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar

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.

Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data

2 code implementations23 Feb 2023 Alicia Curth, Mihaela van der Schaar

We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events.

In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation

1 code implementation6 Feb 2023 Alicia Curth, Mihaela van der Schaar

Personalized treatment effect estimates are often of interest in high-stakes applications -- thus, before deploying a model estimating such effects in practice, one needs to be sure that the best candidate from the ever-growing machine learning toolbox for this task was chosen.

Model Selection

Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions

no code implementations11 Aug 2022 Alicia Curth, Alihan Hüyük, Mihaela van der Schaar

We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability

no code implementations16 Jun 2022 Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar

This allows us to evaluate treatment effect estimators along a new and important dimension that has been overlooked in previous work: We construct a benchmarking environment to empirically investigate the ability of personalized treatment effect models to identify predictive covariates -- covariates that determine differential responses to treatment.

Benchmarking Feature Importance

Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies

2 code implementations ICLR 2022 Alex J. Chan, Alicia Curth, Mihaela van der Schaar

Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decision-maker's ability to perform a task, e. g. to alert them to potential biases or oversights on their part.

Decision Making

Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects

1 code implementation25 Feb 2022 Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar

While observational data is confounded, randomized data is unconfounded, but its sample size is usually too small to learn heterogeneous treatment effects.

Representation Learning

Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time

no code implementations7 Dec 2021 Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin McKinney, Mihaela van der Schaar

Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time.

Estimating Multi-cause Treatment Effects via Single-cause Perturbation

1 code implementation NeurIPS 2021 Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar

Most existing methods for conditional average treatment effect estimation are designed to estimate the effect of a single cause - only one variable can be intervened on at one time.

Causal Inference

Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators

no code implementations28 Jul 2021 Alicia Curth, Mihaela van der Schaar

The machine learning toolbox for estimation of heterogeneous treatment effects from observational data is expanding rapidly, yet many of its algorithms have been evaluated only on a very limited set of semi-synthetic benchmark datasets.

BIG-bench Machine Learning

On Inductive Biases for Heterogeneous Treatment Effect Estimation

1 code implementation NeurIPS 2021 Alicia Curth, Mihaela van der Schaar

We investigate how to exploit structural similarities of an individual's potential outcomes (POs) under different treatments to obtain better estimates of conditional average treatment effects in finite samples.

Inductive Bias POS

Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms

2 code implementations26 Jan 2021 Alicia Curth, Mihaela van der Schaar

The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and interest in flexibly investigating effect heterogeneity is growing rapidly.


Estimating Structural Target Functions using Machine Learning and Influence Functions

1 code implementation14 Aug 2020 Alicia Curth, Ahmed M. Alaa, Mihaela van der Schaar

Within this framework, we propose two general learning algorithms that build on the idea of nonparametric plug-in bias removal via IFs: the 'IF-learner' which uses pseudo-outcomes motivated by uncentered IFs for regression in large samples and outputs entire target functions without confidence bands, and the 'Group-IF-learner', which outputs only approximations to a function but can give confidence estimates if sufficient information on coarsening mechanisms is available.

BIG-bench Machine Learning Econometrics +1

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