Skip to content

Workshop

Causal inference with time-varying treatments and confounding

Max Weber Programme Mini-Workshop

Add to calendar 2022-05-25 10:00 2022-05-25 16:00 Europe/Rome Causal inference with time-varying treatments and confounding Sala del Capitolo Badia Fiesolana YYYY-MM-DD
Print

When

Wed 25 May 2022 10.00 - 12.00

Wed 25 May 2022 14.00 - 16.00

Where

Sala del Capitolo

Badia Fiesolana

This mini-workshop introduces causal inference with treatments and confounders that are time-varying. Traditional methods for causal inference were designed for time-constant treatments and confounders. These tools may lead to biased estimates when both treatments and confounders vary over time and in the presence of treatment-confounder feedback.

The workshop's morning session introduces the problem and the afternoon session goes through social science research examples where such problems arise.

Miguel Hernán (Kolokotrones Professor of Biostatistics and Epidemiology, Harvard University) - "Causal diagrams and treatment-confounder feedback"

10:00 - 12:00 | Sala del Capitolo

Traditional methods for confounder adjustment were designed for treatments and confounders that do not vary over time. However, we are often interested in making causal inferences about time-varying treatments, which implies that the confounders are also time-varying and that treatment-confounder feedback may arise. Traditional methods cannot appropriately adjust for confounders in the presence of treatment-confounder feedback. Rather, Robins’s g-methods are needed. The use of causal diagrams is helpful both to describe treatment-confounder feedback and to explain why g-methods are needed.

Alejandra Rodríguez Sánchez (Researcher, Deutsches Zentrum für Integrations- und Migrationsforschung & Humboldt University Berlin) - "Time-varying treatments and confounders and treatment-confounder feedback: examples from social science"

14:00 -16:00 | Seminar Room 2

Time-varying treatments and confounders and treatment-confounder feedback processes abound in the social, political and economic sciences. Examples include sequential treatments (e.g., multiple spells of unemployment, multiple elections) and cumulative (dis)advantage processes ( success-feeds-success ), where precedent treatments affect the confounders of subsequent treatments. Despite the prominence of such processes, methods to tackle the causal inference problems that they lead to have not been widely adopted in the social sciences. This lecture goes through some real-life research examples where these problems have been tackled. It follows up the topics from the more conceptual morning lecture.

Links:

Speaker(s):

Miguel Hernan (Harvard University)

Alejandra Rodríguez Sanchéz (Deutsches Zentrum für Integrations- und Migrationsforschung & Humboldt University Berlin)

Contact(s):

Laura Martignoni

Go back to top of the page