Presentations and talks

Presentations in causal inference group meetings at Erasmus MC Link to heading

[02 Oct 2024] Introduction to proximal causal inference 👉🔗

• Proximal causal inference uses variables around a known-but-unmeasured confounder to attempt to get point estimates of causal effects. How does it work? This presentation explain it in a layperson language.

[01 May 2024] Introduction to standardization 👉🔗

• A brief introduction about standardization, an important class of g-methods used a lot in modern causal inference. This discussion was mainly about Causal Inference What If book chapter 13.

[07 Feb 2024] Causal language use in clinical guidelines for diabetes: evaluation of causality and alignment 👉🔗

• Presentation of the research proposal for my master’s thesis project. How do causal languages and linking words look like in clinical guidelines, and whether they are properly used and interpreted?

Presentations in journal club at Erasmus MC Dept. Epidemiology Link to heading

[20 Sep 2024] Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities

• Data cleaning is a must-do, but is barely mentioned, reported and taught in epidemiological courses. The best paper we can find that discussed this topic was published about two decades ago. Should data cleaning be sent back to epidemiologists’ attention? This is a presentation of the paper Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities (van den Broeck et al., PLoS Medicine 2005;2(10):e267.)

[31 May 2024] Causal machine learning for treatment outcomes 👉🔗

• Estimating causal effect, where counterfactual worlds involve, is always attractive, particularly with machine learning techniques. What can ML do for causal inference and how should we think about it? This is a presentation of the paper Causal machine learning for predicting treatment outcomes (Feuerriegel et al., Nature Medicine 2024;30:958–68).