Search Results for author:
Found 15 papers, 11 papers with code
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.
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
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
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.
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
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
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.
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Consider the problem of imputing missing values in a dataset.
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
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.
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.
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.
Estimating Multi-cause Treatment Effects via Single-cause Perturbation
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.
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
We study the problem of inferring heterogeneous treatment effects from time-to-event data.
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators
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.
On Inductive Biases for Heterogeneous Treatment Effect Estimation
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.
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
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
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.
