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
Introduction
I propose a cutting-edge and transformative paradigm for statistical modelling that is crucial to enhance the quality of data analyses. Leveraging my expertise in causal inference and semiparametric statistics, I will establish the fundamental principles of a comprehensive estimation theory, which maps model parameters onto generic, interpretable, model-free estimands (e.g., association or effect measures) with favourable efficiency bounds. This approach 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, along with 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 utilize learners that optimize strategically chosen loss functions to achieve low variance and high stability, along with confidence intervals that are valid under weak conditions on the learners.
Connection to Developments in Statistics
I will connect to timely, exciting developments in statistics, such as:
- Debiased learning of function-valued parameters
- 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 of Assumption-Lean Modelling
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)
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