Presentations and talks

Defense of my research master thesis Link to heading

  • Clinical practice guidelines are designed to guide clinical practice and involve causal language. Sometimes guidelines make or require stronger causal claims than those in the references they rely on, a phenomenon we refer to as ‘causation jump’. How do these jumps look like and what could be the consequences? 👉🔗

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

[30 Apr 2025] Introduction to negative control population 👉🔗

  • Negative control population is a strong though rarely used tool to rule out unmeasured confounding and to falsify the exclusion restriction criteria in IV analysis. How does it work? This presentation gives some take-home points from this paper (Piccininni & Stensrud 2024).

[02 Apr 2025] Predicting treatment effects in an ovarian cancer screening trial 👉🔗

  • We aim to “save” a trial in which the average treatment effect (ATE) is close to null: despite a lot of coding works and fancy use of g-methods, the results are still not very promising. This is an interim report of my ongoing project.

[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).