Causal inference methods for real-world data

🇬🇧 Policy relevant effects in infectious disease studies

When facing infectious diseases, healthcare professionals must make decisions under uncertainty. Certain considerations are frequently recurring in this process. Knowing whether a vaccine’s effect wanes is important for determining the timing of seasonal flu shots or assigning COVID-19 boosters. Understanding whether a vaccine has heterogeneous effects on different subtypes, such as Malaria variants, has implications for the development of new treatments. Whether vaccinating a subgroup of individuals is sufficient to reach herd immunity, due to spill-over effects, shapes public health policies. Although the previous plain English sentences might seem clear, I will argue that terms like “waning”, “heterogeneous effects”, and “spill-over” are ambiguously interpreted. I will then propose ways to clarify these terms based on theory and methods for causal inference. Using Malaria, HIV, influenza, and COVID-19 as illustrative examples, I will show that these clarifications matter in practice. In particular, I will leverage the clarifications to define new treatment effects in vaccine settings, which sometimes justify existing analytic approaches and also motivate new methods that are feasible to implement.

🇬🇧 Applications of causal inference in public policy

🇬🇧 Causal inference and AI

🇬🇧 Causal inference practices and problems in psychology

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🇬🇧 “Causal AI”: Is causal inference from healthcare data about to be automated?

Decisions about the treatment and prevention of disease are guided by causal inference and health researchers often make causal inferences using healthcare databases. The emergence of tools referred to as “AI” may transform the way in which those databases are used for causal inference. However, for “AI” to speed up the causal learning for healthcare databases, we need a better understanding of what both “AI” and causal inference are. This talk dissects the components of “Causal AI” and
discusses its potential to automate causal research in the health sciences.

🇬🇧 Causal inference with compositional data

Compositional data is a form of hierarchical data in which a whole (or a total) is the sum of its constituent components. Although compositional data can arise in any setting, they are particularly common in health research, with typical examples including dietary, physical activity, or microbiome data. This type of data can bring a range of analytical and interpretational challenges which risks results being misinterpreted.

In this talk, I will introduce compositional data using directed acrylic graphs (DAGs), outline the different types of causal effects that may be of interest in such data, and discuss suitable analytical approaches.

🇬🇧  Causal Inference Methods for Real-world data

Estimating causal effects in non-experimental data is a key aim of applied health and social science research. Unfortunately, it is also notoriously difficult.

Contemporary causal inference methods, including directed acyclic graphs, promise to revolutionise the analysis and interpretation
non-experimental data, not least by making our ambitions and assumptions far more explicit. In the health sciences, these methods are rapidly gaining popularity, but they are still yet to be adopted as widely as other entrenched
methods.

This talk offers a simple introduction to the new
science of causal inference, with a particular focus on directed acyclic graphs.