SubsidieMeestersAssumption-Lean (Causal) Modelling and Estimation: A Paradigm Shift from Traditional Statistical Modelling
Develop a flexible 'assumption-lean modelling' framework for causal inference that minimizes bias and enhances interpretability in statistical analyses using debiased learning techniques.
Projectdetails
I propose a cutting-edge and transformative paradigm for statistical modelling that is crucial to enhance the quality of data analyses.
Introduction
Leveraging my expertise in causal inference and semiparametric statistics, I will establish the fundamental principles of a comprehensive estimation theory. This theory maps model parameters onto generic, interpretable, model-free estimands (e.g., association or effect measures) with favourable efficiency bounds. Additionally, it harnesses the power of debiased (statistical/machine) learning techniques to estimate these.
Core Objective
My core objective is to develop a flexible and accessible data modelling framework, called ‘assumption-lean modelling’. This framework will deliver minimal bias and maximal interpretability, even in the presence of model misspecification. It will also provide honest confidence bounds that account for model uncertainty.
Debiased Learning
Debiased learning is at the core of this research. While gaining popularity, a rigorous scientific optimality theory is lacking. I shall draw on my expertise in (bias-reduced) double robust estimation to develop optimal debiased learning estimators. These estimators will utilize learners that optimize strategically chosen loss functions to achieve:
- Low variance
- High stability
Additionally, they will provide confidence intervals that are valid under weak conditions on the learners.
Timely Developments
I will connect to timely, exciting developments in statistics, such as debiased learning of function-valued parameters and the construction of confidence bounds for such parameters. I will offer novel avenues into these problems by incorporating the assumption-lean modelling principles and connecting to real-world needs.
Application Areas
I will develop assumption-lean modelling strategies to tackle significant challenges in causal modelling, including:
- Target trial emulation
- Causal mediation analysis
- Statistical modelling of dependent outcomes
I will deliver methods with potential impact on all empirical sciences, as well as on the foundations of the discipline of statistical modelling.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 2.445.063 |
| Totale projectbegroting | € 2.445.063 |
Tijdlijn
| Startdatum | 1-10-2024 |
| Einddatum | 30-9-2029 |
| Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- UNIVERSITEIT GENTpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
High-dimensional nonparametric Bayesian causal inferenceDevelop Bayesian nonparametric methods for high-dimensional causal inference to enhance variable selection and uncertainty quantification, enabling reliable causal conclusions across various fields. | ERC Starting... | € 1.499.770 | 2023 | Details |
Flexible Statistical InferenceDevelop a flexible statistical theory allowing post-hoc data collection and decision-making with error control, utilizing e-values for improved inference in small samples. | ERC Advanced... | € 2.499.461 | 2024 | Details |
Uniting Statistical Testing and Machine Learning for Safe PredictionsThe project aims to enhance the interpretability and reliability of machine learning predictions by integrating statistical methods to establish robust error bounds and ensure safe deployment in real-world applications. | ERC Starting... | € 1.500.000 | 2024 | Details |
Making sense of the senses: Causal Inference in a complex dynamic multisensory worldThis project aims to uncover how the brain approximates causal inference in complex multisensory environments using interdisciplinary methods, potentially informing AI and addressing perceptual challenges in clinical populations. | ERC Advanced... | € 2.499.527 | 2024 | Details |
A New Bayesian Foundation for Psychometric Network ModellingThis project aims to enhance psychological network modelling by developing a Bayesian confirmatory methodology with model-averaging for robust, replicable results, implemented in user-friendly software. | ERC Starting... | € 1.499.991 | 2022 | Details |
High-dimensional nonparametric Bayesian causal inference
Develop Bayesian nonparametric methods for high-dimensional causal inference to enhance variable selection and uncertainty quantification, enabling reliable causal conclusions across various fields.
Flexible Statistical Inference
Develop a flexible statistical theory allowing post-hoc data collection and decision-making with error control, utilizing e-values for improved inference in small samples.
Uniting Statistical Testing and Machine Learning for Safe Predictions
The project aims to enhance the interpretability and reliability of machine learning predictions by integrating statistical methods to establish robust error bounds and ensure safe deployment in real-world applications.
Making sense of the senses: Causal Inference in a complex dynamic multisensory world
This project aims to uncover how the brain approximates causal inference in complex multisensory environments using interdisciplinary methods, potentially informing AI and addressing perceptual challenges in clinical populations.
A New Bayesian Foundation for Psychometric Network Modelling
This project aims to enhance psychological network modelling by developing a Bayesian confirmatory methodology with model-averaging for robust, replicable results, implemented in user-friendly software.